-|`model`|You can set the `model` property to use a specific base model or [Custom Speech](how-to-custom-speech-train-model.md) model. If you don't specify the `model`, the default base model for the locale is used. For more information, see [Using custom models](#using-custom-models) and [Using Whisper models](#using-whisper-models).|

-Optionally, you can modify the previous [create transcription example](#create-a-batch-transcription) by setting the `model` property to use a specific base model or [Custom Speech](how-to-custom-speech-train-model.md) model.

-To use a Custom Speech model for batch transcription, you need the model's URI. You can retrieve the model location when you create or get a model. The top-level `self` property in the response body is the model's URI. For an example, see the JSON response example in the [Create a model](how-to-custom-speech-train-model.md?pivots=rest-api#create-a-model) guide.

-Batch transcription requests for expired models fail with a 4xx error. You want to set the `model` property to a base model or custom model that hasn't yet expired. Otherwise don't include the `model` property to always use the latest base model. For more information, see [Choose a model](how-to-custom-speech-create-project.md#choose-your-model) and [Custom Speech model lifecycle](how-to-custom-speech-model-and-endpoint-lifecycle.md).

-When using BYOS, the Speech service doesn't keep any customer artifacts after the data processing (transcription, model training, model testing) is complete. However, some metadata that isn't derived from the user content is stored within Speech service premises. For example, in the Custom speech scenario, the Service keeps certain information about the custom endpoints, like which models they use.

-With Custom speech, you can evaluate and improve the accuracy of speech recognition for your applications and products. A custom speech model can be used for real-time speech to text, speech translation, and batch transcription. For more information, see the [Custom speech overview](custom-speech-overview.md).

-There's nothing specific about how you use Custom speech with BYOS-enabled Speech resource. The only difference is where all custom model related data, which Speech service collects and produces for you, is stored. The data is stored in the following Blob containers of BYOS-associated Storage account:

-> Speech service relies on pre-defined Blob container paths and file names for Custom speech module to correctly function. Don't move, rename or in any way alter the contents of `customspeech-models` container and Custom speech related folders of `customspeech-artifacts` container.

-> Use standard tools, like REST API and Speech Studio to interact with the Custom speech related data. See details in [Custom speech section](custom-speech-overview.md).

-### Use of REST API with Custom speech

-| [Custom Speech](./custom-speech-overview.md) | A speech to text feature used to evaluate and improve the speech recognition accuracy of use-case specific entities (such as alpha-numeric customer, case, and contract IDs, license plates, and names). You can also train a custom model with your own product names and industry terminology. |

-description: Custom Speech is a set of online tools that allows you to evaluate and improve the speech to text accuracy for your applications, tools, and products.

-# What is Custom Speech?

-With Custom Speech, you can evaluate and improve the accuracy of speech recognition for your applications and products. A custom speech model can be used for [real-time speech to text](speech-to-text.md), [speech translation](speech-translation.md), and [batch transcription](batch-transcription.md).

-A custom model can be used to augment the base model to improve recognition of domain-specific vocabulary specific to the application by providing text data to train the model. It can also be used to improve recognition based for the specific audio conditions of the application by providing audio data with reference transcriptions.

-With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.

-

-1. [Create a project](how-to-custom-speech-create-project.md) and choose a model. Use a <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesSpeechServices" title="Create a Speech resource" target="_blank">Speech resource</a> that you create in the Azure portal. If you train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. See footnotes in the [regions](regions.md#speech-service) table for more information.

- > You pay for Custom Speech model usage and [endpoint hosting](how-to-custom-speech-deploy-model.md). You'll also be charged for custom speech model training if the base model was created on October 1, 2023 and later. You are not charged for training if the base model was created prior to October 2023. For more information, see [Azure AI Speech pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/) and the [Charge for adaptation section in the speech to text 3.2 migration guide](./migrate-v3-1-to-v3-2.md#charge-for-adaptation).

-1. [Deploy a model](how-to-custom-speech-deploy-model.md). Once you're satisfied with the test results, deploy the model to a custom endpoint. Except for [batch transcription](batch-transcription.md), you must deploy a custom endpoint to use a Custom Speech model.

- > A hosted deployment endpoint isn't required to use Custom Speech with the [Batch transcription API](batch-transcription.md). You can conserve resources if the custom speech model is only used for batch transcription. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).

-An AI system includes not only the technology, but also the people who use it, the people who will be affected by it, and the environment in which it's deployed. Read the transparency notes to learn about responsible AI use and deployment in your systems.

-description: Apply DevOps with Custom Speech and CI/CD workflows. Implement an existing DevOps solution for your own project.

-# CI/CD for Custom Speech

-Implement automated training, testing, and release management to enable continuous improvement of Custom Speech models as you apply updates to training and testing data. Through effective implementation of CI/CD workflows, you can ensure that the endpoint for the best-performing Custom Speech model is always available.

-[Continuous integration](/devops/develop/what-is-continuous-integration) (CI) is the engineering practice of frequently committing updates in a shared repository, and performing an automated build on it. CI workflows for Custom Speech train a new model from its data sources and perform automated testing on the new model to ensure that it performs better than the previous model.

-[Continuous delivery](/devops/deliver/what-is-continuous-delivery) (CD) takes models from the CI process and creates an endpoint for each improved Custom Speech model. CD makes endpoints easily available to be integrated into solutions.

-## CI/CD workflows for Custom Speech

-The purpose of these workflows is to ensure that each Custom Speech model has better recognition accuracy than the previous build. If the updates to the testing and/or training data improve the accuracy, these workflows create a new Custom Speech endpoint.

-Git servers such as GitHub and Azure DevOps can run automated workflows when specific Git events happen, such as merges or pull requests. For example, a CI workflow can be triggered when updates to testing data are pushed to the *main* branch. Different Git Servers will have different tooling, but will allow scripting command-line interface (CLI) commands so that they can execute on a build server.

-Along the way, the workflows should name and store data, tests, test files, models, and endpoints such that they can be traced back to the commit or version they came from. It is also helpful to name these assets so that it is easy to see which were created after updating testing data versus training data.

-The principal purpose of the CI/CD workflows is to build a new model using the training data, and to test that model using the testing data to establish whether the [Word Error Rate](how-to-custom-speech-evaluate-data.md#evaluate-word-error-rate-wer) (WER) has improved compared to the previous best-performing model (the "benchmark model"). If the new model performs better, it becomes the new benchmark model against which future models are compared.

-The CI workflow for testing data updates should retest the current benchmark model with the updated test data to calculate the revised WER. This ensures that when the WER of a new model is compared to the WER of the benchmark, both models have been tested against the same test data and you're comparing like with like.

-### Tools for Custom Speech workflows

-Use the following tools for CI/CD automation workflows for Custom Speech:

-## DevOps solution for Custom Speech using GitHub Actions

-For an already-implemented DevOps solution for Custom Speech, go to the [Speech DevOps template repo](https://github.com/Azure-Samples/Speech-Service-DevOps-Template). Create a copy of the template and begin development of custom models with a robust DevOps system that includes testing, training, and versioning using GitHub Actions. The repository provides sample testing and training data to aid in setup and explain the workflow. After initial setup, replace the sample data with your project data.

-description: Learn about how to create a project for Custom Speech.

-# Create a Custom Speech project

-Custom Speech projects contain models, training and testing datasets, and deployment endpoints. Each project is specific to a [locale](language-support.md?tabs=stt). For example, you might create a project for English in the United States.

-To create a Custom Speech project, follow these steps:

-Select the new project by name or select **Go to project**. You will see these menu items in the left panel: **Speech datasets**, **Train custom models**, **Test models**, and **Deploy models**.

-There are a few approaches to using Custom Speech models:

-One recommended way to see if the base model will suffice is to analyze the transcription produced from the base model and compare it with a human-generated transcript for the same audio. You can compare the transcripts and obtain a [word error rate (WER)](how-to-custom-speech-evaluate-data.md#evaluate-word-error-rate-wer) score. If the WER score is high, training a custom model to recognize the incorrectly identified words is recommended.

-A base model or custom model deployed to an endpoint using Custom Speech is fixed until you decide to update it. The speech recognition accuracy and quality will remain consistent, even when a new base model is released. This allows you to lock in the behavior of a specific model until you decide to use a newer model.

-description: Learn how to deploy Custom Speech models.

-# Deploy a Custom Speech model

-In this article, you'll learn how to deploy an endpoint for a Custom Speech model. With the exception of [batch transcription](batch-transcription.md), you must deploy a custom endpoint to use a Custom Speech model.

-> A hosted deployment endpoint isn't required to use Custom Speech with the [Batch transcription API](batch-transcription.md). You can conserve resources if the [custom speech model](how-to-custom-speech-train-model.md) is only used for batch transcription. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).

-1. Select **Custom Speech** > Your project name > **Deploy models**.

- If this is your first endpoint, you'll notice that there are no endpoints listed in the table. After you create an endpoint, you use this page to track each deployed endpoint.

-1. Select **Custom Speech** > Your project name > **Deploy models**.

-The redeployment takes several minutes to complete. In the meantime, your endpoint will use the previous model without interruption of service.

-1. Select **Custom Speech** > Your project name > **Deploy models**.

-The location of each log file with more details are returned in the response body.

-The location of each log file with more details are returned in the response body.

-Logging data is available on Microsoft-owned storage for 30 days, after which it will be removed. If your own storage account is linked to the Azure AI services subscription, the logging data won't be automatically deleted.

-The base builders of the display text pipeline are maintained by Microsoft for the general purpose display processing tasks. You get the base builders by default when you use the Speech service. For more information about out-of-the-box formatting, see [Display text format](./display-text-format.md).

-Beside the base builders maintained by Microsoft for the general purpose display processing tasks, you can define custom display text formatting rules to customize the display text formatting pipeline for your specific scenarios. The custom display text formatting rules are defined in a custom display text formatting file.

-For example, a developer might have an item (such as a product) named with the alphanumeric form `JO:500`. The job of our system will be to figure out that users might say the letter part as `J O`, or they might say `joe`, and the number part as `five hundred` or `five zero zero` or `five oh oh` or `five double zero`, and then build a model that maps all of these possibilities back to `JO:500` (including inserting the colon).

-Patterns can be applied in parallel by specifying one rule per line in the display text formatting file. Here is an example of a display text formatting file that specifies two rules:

-Suppose a customer needs to refer to a whole series of alphanumeric items named `JO:500`, `JO:600`, `JO:700`, etc. We can support this without requiring spelling out all possibilities in several ways.

-Like a regular expression, there are several predefined character classes for an ITN rule:

-The general philosophy is "you show us what the output should look like, and the Speech service figures out how people say it." But this doesn't always work because some scenarios might have quirky unpredictable ways of saying things, or the Speech service background rules might have gaps. For example, there can be colloquial pronunciations for initials and acronyms--`ZPI` might be spoken as `zippy`. In this case a pattern like `ZPI-\d\d` is unlikely to work if a user says `zippy twenty two`. For this sort of situation, there's a display text format notation `{spoken>written}`. This particular case could be written out `{zippy>ZPI}-\d\d`.

-# Test accuracy of a Custom Speech model

-1. Select **Custom Speech** > Your project name > **Test models**.

-1. Select **Custom Speech** > Your project name > **Test models**.

-This page lists all the utterances in your dataset and the recognition results, alongside the transcription from the submitted dataset. You can toggle various error types, including insertion, deletion, and substitution. By listening to the audio and comparing recognition results in each column, you can decide which model meets your needs and determine where additional training and improvements are required.

-You can use the WER calculation from the machine recognition results to evaluate the quality of the model you're using with your app, tool, or product. A WER of 5-10% is considered to be good quality and is ready to use. A WER of 20% is acceptable, but you might want to consider additional training. A WER of 30% or more signals poor quality and requires customization and training.

-How the errors are distributed is important. When many deletion errors are encountered, it's usually because of weak audio signal strength. To resolve this issue, you need to collect audio data closer to the source. Insertion errors mean that the audio was recorded in a noisy environment and crosstalk might be present, causing recognition issues. Substitution errors are often encountered when an insufficient sample of domain-specific terms has been provided as either human-labeled transcriptions or related text.

-By analyzing individual files, you can determine what type of errors exist, and which errors are unique to a specific file. Understanding issues at the file level will help you target improvements.

-The formula of TER calculation is also very similar to WER. The only difference is that TER is calculated based on the token level instead of word level.

-In a real-world case, you may analyze both WER and TER results to get the desired improvements.

-description: You use human-labeled transcriptions with your audio data to improve speech recognition accuracy. This is especially helpful when words are deleted or incorrectly replaced.

-A large sample of transcription data is required to improve recognition. We suggest providing between 1 and 20 hours of audio data. The Speech service will use up to 20 hours of audio for training. This guide is broken up by locale, with sections for US English, Mandarin Chinese, and German.

-Human-labeled transcriptions for languages other than English and Mandarin Chinese, must be UTF-8 encoded with a byte-order marker. For other locales transcription requirements, see the sections below.

-| ΓÇ£Hello worldΓÇ¥ | "Hello world" | The opening and closing quotations marks have been substituted with appropriate ASCII characters. |

-| JohnΓÇÖs day | John's day | The apostrophe has been substituted with the appropriate ASCII character. |

-| It was goodΓÇöno, it was great! | it was good--no, it was great! | The em dash was substituted with two hyphens. |

-In Japanese (ja-JP), there's a maximum length of 90 characters for each sentence. Lines with longer sentences will be discarded. To add longer text, insert a period in between.

-| "你好" | "你好" | The opening and closing quotations marks have been substituted with appropriate characters. |

-| 需要什么帮助? | 需要什么帮助？| The question mark has been substituted with appropriate character. |

-description: Custom Speech lets you qualitatively inspect the recognition quality of a model. You can play back uploaded audio and determine if the provided recognition result is correct.

-# Test recognition quality of a Custom Speech model

-You can inspect the recognition quality of a Custom Speech model in the [Speech Studio](https://aka.ms/speechstudio/customspeech). You can play back uploaded audio and determine if the provided recognition result is correct. After a test has been successfully created, you can see how a model transcribed the audio dataset, or compare results from two models side by side.

-1. Navigate to **Speech Studio** > **Custom Speech** and select your project name from the list.

-1. Select **Custom Speech** > Your project name > **Test models**.

-This page lists all the utterances in your dataset and the recognition results, alongside the transcription from the submitted dataset. You can toggle various error types, including insertion, deletion, and substitution. By listening to the audio and comparing recognition results in each column, you can decide which model meets your needs and determine where additional training and improvements are required.

-1. Select **Custom Speech** > Your project name > **Test models**.

-If the test dataset included multiple audio files, you'll see multiple rows in the table. If you included two models in the test, transcriptions are shown in side-by-side columns. Transcription differences between models are shown in blue text font.

-The audio test dataset, transcriptions, and models tested are returned in the [test results](#get-test-results). If only one model was tested, the `model1` value will match `model2`, and the `transcription1` value will match `transcription2`.

-The audio test dataset, transcriptions, and models tested are returned in the [test results](#get-test-results). If only one model was tested, the `model1` value will match `model2`, and the `transcription1` value will match `transcription2`.

-description: Custom Speech provides base models for training and lets you create custom models from your data. This article describes the timelines for models and for endpoints that use these models.

-# Custom Speech model lifecycle

-You can use a Custom Speech model for some time after it's deployed to your custom endpoint. But when new base models are made available, the older models are expired. You must periodically recreate and train your custom model from the latest base model to take advantage of the improved accuracy and quality.

-In this context, quarters end on January 15th, April 15th, July 15th, and October 15th.

-When a custom model or base model expires, it is no longer available for transcription. You can change the model that is used by your custom speech endpoint without downtime.

-|Custom endpoint|Speech recognition requests will fall back to the most recent base model for the same [locale](language-support.md?tabs=stt). You will get results, but recognition might not accurately transcribe your domain data. |Update the endpoint's model as described in the [Deploy a Custom Speech model](how-to-custom-speech-deploy-model.md) guide. |

-|Batch transcription |[Batch transcription](batch-transcription.md) requests for expired models will fail with a 4xx error. |In each [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) REST API request body, set the `model` property to a base model or custom model that hasn't yet expired. Otherwise don't include the `model` property to always use the latest base model. |

-The last date that you could use the base model for training was shown when you created the custom model. For more information, see [Train a Custom Speech model](how-to-custom-speech-train-model.md).

-1. Select **Custom Speech** > Your project name > **Deploy models**.

-1. The expiration date for the model is shown in the **Expiration** column. This is the last date that you can use the model for transcription.

-In the response, take note of the date in the `adaptationDateTime` property. This is the last date that you can use the base model for training. Also take note of the date in the `transcriptionDateTime` property. This is the last date that you can use the base model for transcription.

-In the response, take note of the date in the `adaptationDateTime` property. This is the last date that you can use the base model for training. Also take note of the date in the `transcriptionDateTime` property. This is the last date that you can use the base model for transcription.

-1. Select **Custom Speech** > Your project name > **Train custom models**.

-1. The expiration date the custom model is shown in the **Expiration** column. This is the last date that you can use the custom model for transcription. Base models are not shown on the **Train custom models** page.

-1. Select **Custom Speech** > Your project name > **Deploy models**.

-1. The expiration date for the model is shown in the **Expiration** column. This is the last date that you can use the model for transcription.

-In the response, take note of the date in the `transcriptionDateTime` property. This is the last date that you can use your custom model for transcription. The `adaptationDateTime` property is not applicable, since custom models are not used to train other custom models.

-In the response, take note of the date in the `transcriptionDateTime` property. This is the last date that you can use your custom model for transcription. The `adaptationDateTime` property is not applicable, since custom models are not used to train other custom models.

-description: Learn about types of training and testing data for a Custom Speech project, along with how to use and manage that data.

-In a Custom Speech project, you can upload datasets for training, qualitative inspection, and quantitative measurement. This article covers the types of training and testing data that you can use for Custom Speech.

-* Include text and audio data to cover the kinds of verbal statements that your users will make when they're interacting with your model. For example, a model that raises and lowers the temperature needs training on statements that people might make to request such changes.

-* Include samples from different environments, for example, indoor, outdoor, and road noise, where your model will be used.

-* Record audio with hardware devices that the production system will use. If your model must identify speech recorded on devices of varying quality, the audio data that you provide to train your model must also represent these diverse scenarios.

-The following table lists accepted data types, when each data type should be used, and the recommended quantity. Not every data type is required to create a model. Data requirements will vary depending on whether you're creating a test or training a model.

-> Start with small sets of sample data that match the language, acoustics, and hardware where your model will be used. Small datasets of representative data can expose problems before you invest in gathering larger datasets for training. For sample Custom Speech data, see <a href="https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/customspeech" target="_target">this GitHub repository</a>.

-If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. See footnotes in the [regions](regions.md#speech-service) table for more information. In regions with dedicated hardware for Custom Speech training, the Speech service will use up to 20 hours of your audio training data, and can process about 10 hours of data per day. In other regions, the Speech service uses up to 8 hours of your audio data, and can process about 1 hour of data per day. After the model is trained, you can copy the model to another region as needed with the [Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo) REST API.

-A model that's trained on a subset of scenarios can perform well in only those scenarios. Carefully choose data that represents the full scope of scenarios that you need your custom model to recognize. The following table shows datasets to consider for some speech recognition scenarios:

-For a list of base models that support training with audio data, see [Language support](language-support.md?tabs=stt). Even if a base model does support training with audio data, the service might use only part of the audio. And it will still use all the transcripts.

-* Training with audio will bring the most benefits if the audio is also hard to understand for humans. In most cases, you should start training by using only related text.

-* If you use one of the most heavily used languages, such as US English, it's unlikely that you would need to train with audio data. For such languages, the base models already offer very good recognition results in most scenarios, so it's probably enough to train with related text.

-* Custom Speech can capture word context only to reduce substitution errors, not insertion or deletion errors.

-* It can take several days for a training operation to finish. To improve the speed of training, be sure to create your Speech service subscription in a region that has dedicated hardware for training.

-A large training dataset is required to improve recognition. Generally, we recommend that you provide word-by-word transcriptions for 1 to 20 hours of audio. However, even as little as 30 minutes can help improve recognition results. Although creating human-labeled transcription can take time, improvements in recognition will only be as good as the data that you provide. You should upload only high-quality transcripts.

-Audio files can have silence at the beginning and end of the recording. If possible, include at least a half-second of silence before and after speech in each sample file. Although audio with low recording volume or disruptive background noise is not helpful, it shouldn't limit or degrade your custom model. Always consider upgrading your microphones and signal processing hardware before gathering audio samples.

-Custom Speech projects require audio files with these properties:

-| Maximum length per audio | 2 hours (testing) / 60 s (training)<br/><br/>Training with audio has a maximum audio length of 60 seconds per file. For audio files longer than 60 seconds, only the corresponding transcription files will be used for training. If all audio files are longer than 60 seconds, the training will fail.|

-* Avoid repeating characters, words, or groups of words more than three times, as in "aaaa," "yeah yeah yeah yeah," or "that's it that's it that's it that's it." The Speech service might drop lines with too many repetitions.

-|`speech:phoneticlexicon`|A list of phonetic pronunciations according to the [Universal Phone Set](customize-pronunciation.md). Pronunciation is adjusted for each instance where the word appears in a list or training sentence. For example, if you have a word that sounds like "cat" and you want to adjust the pronunciation to "k ae t", you would add `- cat/k ae t` to the `speech:phoneticlexicon` list.|Maximum of 15,000 entries. Maximum of 2 pronunciations per word.|

-|`#ExampleSentences`|A pound symbol (`#`) delimits a section of example sentences. The section heading can only contain letters, digits, and underscores. Example sentences should reflect the range of speech that your model should expect. A training sentence can refer to items under a `@list` by using surrounding left and right curly braces (`{@list name}`). You can refer to multiple lists in the same training sentence, or none at all.|Maximum file size of 200MB.|

-Audio data is optimal for testing the accuracy of Microsoft's baseline speech to text model or a custom model. Keep in mind that audio data is used to inspect the accuracy of speech with regard to a specific model's performance. If you want to quantify the accuracy of a model, use [audio + human-labeled transcripts](#audio--human-labeled-transcript-data-for-training-or-testing).

-Custom Speech projects require audio files with these properties:

-| Maximum length per audio | 2 hours |

-Automatic Speech Recognition output display format is critical to downstream tasks and one-size doesnΓÇÖt fit all. Adding Custom Display Format rules allows users to define their own lexical-to-display format rules to improve the speech recognition service quality on top of Microsoft Azure Custom Speech Service.

-|#test|A list of unit test cases to validate if the display rules work as expected, including the lexical format input and the expected display format output.|Maximum file size of 10MB|

-description: Learn how to train Custom Speech models. Training a speech to text model can improve recognition accuracy for the Microsoft base model or a custom model.

-# Train a Custom Speech model

-In this article, you'll learn how to train a custom model to improve recognition accuracy from the Microsoft base model. The speech recognition accuracy and quality of a Custom Speech model will remain consistent, even when a new base model is released.

-> You pay for Custom Speech model usage and [endpoint hosting](how-to-custom-speech-deploy-model.md). You'll also be charged for custom speech model training if the base model was created on October 1, 2023 and later. You are not charged for training if the base model was created prior to October 2023. For more information, see [Azure AI Speech pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/) and the [Charge for adaptation section in the speech to text 3.2 migration guide](./migrate-v3-1-to-v3-2.md#charge-for-adaptation).

-Training a model is typically an iterative process. You will first select a base model that is the starting point for a new model. You train a model with [datasets](./how-to-custom-speech-test-and-train.md) that can include text and audio, and then you test. If the recognition quality or accuracy doesn't meet your requirements, you can create a new model with additional or modified training data, and then test again.

-You can use a custom model for a limited time after it's trained. You must periodically recreate and adapt your custom model from the latest base model to take advantage of the improved accuracy and quality. For more information, see [Model and endpoint lifecycle](./how-to-custom-speech-model-and-endpoint-lifecycle.md).

-> In regions with dedicated hardware for Custom Speech training, the Speech service will use up to 20 hours of your audio training data, and can process about 10 hours of data per day. In other regions, the Speech service uses up to 8 hours of your audio data, and can process about 1 hour of data per day. See footnotes in the [regions](regions.md#speech-service) table for more information.

-After you've uploaded [training datasets](./how-to-custom-speech-test-and-train.md), follow these instructions to start training your model:

-1. Select **Custom Speech** > Your project name > **Train custom models**.

-1. Select **Custom Speech** > Your project name > **Train custom models**.

-Copying a model directly to a project in another region is not supported with the Speech CLI. You can copy a model to a project in another region using the [Speech Studio](https://aka.ms/speechstudio/customspeech) or [Speech to text REST API](rest-speech-to-text.md).

-If you are prompted in Speech Studio, you can connect them by selecting the **Connect** button.

-description: The online transcription editor allows you to create or edit audio + human-labeled transcriptions for Custom Speech.

-The online transcription editor allows you to create or edit audio + human-labeled transcriptions for Custom Speech. The main use cases of the editor are as follows:

-Datasets in the **Training and testing dataset** tab can't be updated. You can import a copy of a training or testing dataset to the **Editor** tab, add or edit human-labeled transcriptions to match the audio, and then export the edited dataset to the **Training and testing dataset** tab. Please also note that you can't use a dataset that's in the Editor to train or test a model.

-1. Select **Custom Speech** > Your project name > **Speech datasets** > **Editor**.

-1. Review your settings, and then select **Import and close** to kick off the import process. After data has been successfully imported, you can select datasets and start editing.

-Once a dataset has been imported to the Editor, you can start editing the dataset. You can add or edit human-labeled transcriptions to match the audio as you hear it. You do not edit any audio data.

-1. Select **Custom Speech** > Your project name > **Speech datasets** > **Editor**.

-1. After you've made edits, select **Save**.

-1. Select **Custom Speech** > Your project name > **Speech datasets** > **Editor**.

-The files are exported as a new dataset, and will not impact or replace other training or testing datasets.

-description: Learn about how to upload data to test or train a Custom Speech model.

-# Upload training and testing datasets for Custom Speech

-1. Select **Custom Speech** > Your project name > **Speech datasets** > **Upload data**.

-1. Specify the dataset location, and then select **Next**. You can choose a local file or enter a remote location such as Azure Blob URL. If you select remote location, and you don't use trusted Azure services security mechanism (see next Note), then the remote location should be a URL that can be retrieved with a simple anonymous GET request. For example, a [SAS URL](/azure/storage/common/storage-sas-overview) or a publicly accessible URL. URLs that require extra authorization, or expect user interaction are not supported.

-> Connecting a dataset to a Custom Speech project isn't required to train and test a custom model using the REST API or Speech CLI. But if the dataset is not connected to any project, you can't select it for training or testing in the [Speech Studio](https://aka.ms/speechstudio/customspeech).

-When you're ready to create a custom Text to speech voice for your application, the first step is to gather audio recordings and associated scripts to start training the voice model. The Speech service uses this data to create a unique voice tuned to match the voice in the recordings. After you've trained the voice, you can start synthesizing speech in your applications.

-In some cases, you may not have the right dataset ready and will want to test the custom neural voice training with available audio files, short or long, with or without transcripts.

-| File name | File name characters supported by Windows OS, with .wav extension.<br>The characters \ / : * ? " < > \| aren't allowed. <br>It can't start or end with a space, and can't start with a dot. <br>No duplicate file names allowed. |

-| Sampling rate | When creating a custom neural voice, 24,000 Hz is required. |

-> The default sampling rate for a custom neural voice is 24,000 Hz. Audio files with a sampling rate lower than 16,000 Hz will be rejected. If a .zip file contains .wav files with different sample rates, only those equal to or higher than 16,000 Hz will be imported. Your audio files with a sampling rate higher than 16,000 Hz and lower than 24,000 Hz will be up-sampled to 24,000 Hz to train a neural voice. ItΓÇÖs recommended that you should use a sample rate of 24,000 Hz for your training data.

-| # of utterances per line | **One** - Each line of the transcription file should contain the name of one of the audio files, followed by the corresponding transcription. The file name and transcription should be separated by a tab (\t). |

-Below is an example of how the transcripts are organized utterance by utterance in one .txt file:

-ItΓÇÖs important that the transcripts are 100% accurate transcriptions of the corresponding audio. Errors in the transcripts will introduce quality loss during the training.

-In some cases, you may not have segmented audio available. The Speech Studio can help you segment long audio files and create transcriptions. The long-audio segmentation service will use the [Batch Transcription API](batch-transcription.md) feature of speech to text.

-During the processing of the segmentation, your audio files and the transcripts will also be sent to the Custom Speech service to refine the recognition model so the accuracy can be improved for your data. No data will be retained during this process. After the segmentation is done, only the utterances segmented and their mapping transcripts will be stored for your downloading and training.

-| File name | File name characters supported by Windows OS, with .wav extension. <br>The characters \ / : * ? " < > \| aren't allowed. <br>It can't start or end with a space, and can't start with a dot. <br>No duplicate file names allowed. |

-| Sampling rate | When creating a custom neural voice, 24,000 Hz is required. |

-| Sample format |RIFF(.wav): PCM, at least 16-bit<br>mp3: at least 256 KBps bit rate|

-> The default sampling rate for a custom neural voice is 24,000 Hz. Audio files with a sampling rate lower than 16,000 Hz will be rejected. Your audio files with a sampling rate higher than 16,000 Hz and lower than 24,000 Hz will be up-sampled to 24,000 Hz to train a neural voice. ItΓÇÖs recommended that you should use a sample rate of 24,000 Hz for your training data.

-All audio files should be grouped into a zip file. ItΓÇÖs OK to put .wav files and .mp3 files into one audio zip. For example, you can upload a zip file containing an audio file named ΓÇÿkingstory.wavΓÇÖ, 45 second long, and another audio named ΓÇÿqueenstory.mp3ΓÇÖ, 200 second long. All .mp3 files will be transformed into the .wav format after processing.

-All transcripts files in this data type should be grouped into a zip file. For example, you've uploaded a zip file containing an audio file named ΓÇÿkingstory.wavΓÇÖ, 45 seconds long, and another one named ΓÇÿqueenstory.mp3ΓÇÖ, 200 seconds long. You'll need to upload another zip file containing two transcripts, one named ΓÇÿkingstory.txtΓÇÖ, the other one ΓÇÿqueenstory.txtΓÇÖ. Within each plain text file, you'll provide the full correct transcription for the matching audio.

-After your dataset is successfully uploaded, we'll help you segment the audio file into utterances based on the transcript provided. You can check the segmented utterances and the matching transcripts by downloading the dataset. Unique IDs will be assigned to the segmented utterances automatically. ItΓÇÖs important that you make sure the transcripts you provide are 100% accurate. Errors in the transcripts can reduce the accuracy during the audio segmentation and further introduce quality loss in the training phase that comes later.

-If you don't have transcriptions for your audio recordings, use the **Audio only** option to upload your data. Our system can help you segment and transcribe your audio files. Keep in mind, this service will be charged toward your speech to text subscription usage.

-| File name | File name characters supported by Windows OS, with .wav extension. <br>The characters \ / : * ? " < > \| aren't allowed. <br>It can't start or end with a space, and can't start with a dot. <br>No duplicate file names allowed. |

-| Sampling rate | When creating a custom neural voice, 24,000 Hz is required. |

-| Sample format |RIFF(.wav): PCM, at least 16-bit<br>mp3: at least 256 KBps bit rate|

-> The default sampling rate for a custom neural voice is 24,000 Hz. Your audio files with a sampling rate higher than 16,000 Hz and lower than 24,000 Hz will be up-sampled to 24,000 Hz to train a neural voice. ItΓÇÖs recommended that you should use a sample rate of 24,000 Hz for your training data.

-All audio files should be grouped into a zip file. Once your dataset is successfully uploaded, we'll help you segment the audio file into utterances based on our speech batch transcription service. Unique IDs will be assigned to the segmented utterances automatically. Matching transcripts will be generated through speech recognition. All .mp3 files will be transformed into the .wav format after processing. You can check the segmented utterances and the matching transcripts by downloading the dataset.

-In this how-to article, you learn how to develop and configure Custom Commands applications. The Custom Commands feature helps you build rich voice-command apps that are optimized for voice-first interaction experiences. The feature is best suited to task completion or command-and-control scenarios. It's particularly well suited for Internet of Things (IoT) devices and for ambient and headless devices.

-In the empty Smart-Room-Lite Custom Commands application you created, add a command. The command will process an utterance, `Turn on the tv`. It will respond with the message `Ok, turning the tv on`.

-Now add one more command, `SetTemperature`. This command will take a single utterance, `Set the temperature to 40 degrees`, and respond with the message `Ok, setting temperature to 40 degrees`.

-1. Now that the command will handle both on and off scenarios, rename the command as *TurnOnOff*.

- 1. Select **Required**. In the **Add response for a required parameter** window, select **Simple editor**. In the **First variation** field, add *On or Off?*.

-To move the content, start by adding a new web endpoint. In the pane on the left, go to the **Web endpoints** section. There, add a new web endpoint URL. Use the following configuration.

-Then, configure and host a web endpoint that returns a JSON file that lists the devices that can be controlled. The web endpoint should return a JSON file that's formatted like this example:

-Interaction rules are *additional* rules that handle specific or complex situations. Although you're free to author your own interaction rules, in this example you use interaction rules for the following scenarios:

- 1. The condition **All required parameters** has already been added.

-In this section, you'll configure a one-step correction. This correction is used after the fulfillment action has run. You'll also see an example of how a correction is enabled by default if the command isn't fulfilled yet. To add a correction when the command isn't finished, add the new parameter `AlarmTone`.

-The Custom Commands platform allows for one-step correction even when the command has finished. This feature isn't enabled by default. It must be explicitly configured.

-* Output: Ok, turning the tv on

-Now the application will respond in the selected voice, instead of the default voice.

-description: Learn how to get Speech service Speech to text Session ID and Transcription ID

-# How to get Speech to text Session ID and Transcription ID

-If you use [Speech to text](speech-to-text.md) and need to open a support case, you are often asked to provide a *Session ID* or *Transcription ID* of the problematic transcriptions to debug the issue. This article explains how to get these IDs.

-> * *Transcription ID* is used in [Batch transcription](batch-transcription.md).

-In case of [Speech to text REST API for short audio](rest-speech-to-text-short.md) you need to "inject" the session information in the requests. See details in [this section](#provide-session-id-using-rest-api-for-short-audio).

-Open the log file your application produced and look for `SessionId:`. The number that would follow is the Session ID you need. In the following log excerpt example `0b734c41faf8430380d493127bd44631` is the Session ID.

-If you use [Speech CLI](spx-overview.md), then you'll see the Session ID in `SESSION STARTED` and `SESSION STOPPED` console messages.

-Unlike Speech SDK, [Speech to text REST API for short audio](rest-speech-to-text-short.md) does not automatically generate a Session ID. You need to generate it yourself and provide it within the REST request.

-Generate a GUID inside your code or using any standard tool. Use the GUID value *without dashes or other dividers*. As an example we will use `9f4ffa5113a846eba289aa98b28e766f`.

-As a part of your REST request use `X-ConnectionId=<GUID>` expression. For our example, a sample request will look like this:

-`9f4ffa5113a846eba289aa98b28e766f` will be your Session ID.

-> Use the same technique to determine different IDs required for debugging issues related to [Custom Speech](custom-speech-overview.md), like uploading a dataset using [Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Create) request.

-In this article, we will introduce the best practices to lower the latency and bring the best performance to your end users.

-The first byte latency is much lower than finish latency in most cases.

-If the connection is newly established, the network latency will include extra time to establish the connection.

-To pre-connect, establish a connection to the Speech service when you know the connection will be needed soon. For example, if you are building a speech bot in client, you can pre-connect to the speech synthesis service when the user starts to talk, and call `SpeakTextAsync` when the bot reply text is ready.

-When the network is unstable or with limited bandwidth, the payload size will also impact latency.

-You may use load test to test the speech synthesis service capacity and latency.

-Here are some guidelines.

-The custom neural voice lets you build higher-quality voice models while requiring less data. You can develop more realistic, natural, and conversational voices. Your customers and end users will benefit from the latest Text to speech technology, in a responsible way.

-| The standard, or "traditional," method of custom voice breaks down spoken language into phonetic snippets that can be remixed and matched using classical programming or statistical methods. | Custom neural voice synthesizes speech using deep neural networks that have "learned" the way phonetics are combined in natural human speech rather than using classical programming or statistical methods.|

-| Custom voice¹ requires a large volume of voice data to produce a more human-like voice model. With fewer recorded lines, a standard custom voice model will tend to sound more obviously robotic. |The custom neural voice capability enables you to create a unique brand voice in multiple languages and styles by using a small set of recordings.|

-1. Once your application is approved, you will be provided with the access to the "neural" training feature. Make sure you log in to [Speech Studio](https://aka.ms/speechstudio/customvoice) using the same Azure subscription that you provide in your application.

-1. Update your code in your apps if you have created a new endpoint with a new model.

-If you've created a custom voice font, use the endpoint that you've created. You can also use the endpoints listed below, replacing the `{deploymentId}` with the deployment ID for your voice model.

-The Azure AI services [Speech SDK](speech-sdk.md) integrates with the [Language Understanding service (LUIS)](https://www.luis.ai/home) to provide **intent recognition**. An intent is something the user wants to do: book a flight, check the weather, or make a call. The user can use whatever terms feel natural. Using machine learning, LUIS maps user requests to the intents you've defined.

-In this guide, you use the Speech SDK to develop a C# console application that derives intents from user utterances through your device's microphone. You'll learn how to:

-For this guide, you need the prediction key type. This guide uses the example Home Automation LUIS app, which you can create by following the [Use prebuilt Home automation app](../luis/luis-get-started-create-app.md) quickstart. If you've created a LUIS app of your own, you can use it instead.

-When you create a LUIS app, LUIS automatically generates an authoring key so you can test the app using text queries. This key doesn't enable the Speech service integration and won't work with this guide. Create a LUIS resource in the Azure dashboard and assign it to the LUIS app. You can use the free subscription tier for this guide.

-Select the icon next to a key to copy it to the clipboard. (You may use either key.)

-1. Check whether the **.NET desktop development** workload is available. If the workload hasn't been installed, select the check box next to it, and then select **Modify** to start the installation. It may take a few minutes to download and install.

-To add intents, you must provide three arguments: the LUIS model (which has been created and is named `model`), the intent name, and an intent ID. The difference between the ID and the name is as follows.

-The following code illustrates two additional capabilities of intent recognition using the Speech SDK. The first, previously mentioned, is continuous recognition, where the recognizer emits events when results are available. These events can then be processed by event handlers that you provide. With continuous recognition, you call the recognizer's `StartContinuousRecognitionAsync()` method to start recognition instead of `RecognizeOnceAsync()`.

-For example, if you say "Turn off the lights", pause, and then say "Turn on the lights" in your recorded audio file, console output similar to the following may appear:

-# ms.devlang: cpp, csharp, golang, java, javascript, objective-c, python

-# ms.devlang: cpp, csharp, java, javascript, objective-c, python

-> [!Note]

-description: Speech SDK supports viseme events during speech synthesis, which represent key poses in observed speech, such as the position of the lips, jaw, and tongue when producing a particular phoneme.

-# ms.devlang: cpp, csharp, java, javascript, python

-> To explore the locales supported for Viseme ID and blend shapes, refer to [the list of all supported locales](language-support.md?tabs=tts#viseme). Scalable Vector Graphics (SVG) is only supported for the `en-US` locale.

-Viseme ID refers to an integer number that specifies a viseme. We offer 22 different visemes, each depicting the mouth position for a specific set of phonemes. There's no one-to-one correspondence between visemes and phonemes. Often, several phonemes correspond to a single viseme, because they look the same on the speaker's face when they're produced, such as `s` and `z`. For more specific information, see the table for [mapping phonemes to viseme IDs](#map-phonemes-to-visemes).

-Visemes vary by language and locale. Each locale has a set of visemes that correspond to its specific phonemes. The [SSML phonetic alphabets](speech-ssml-phonetic-sets.md) documentation maps viseme IDs to the corresponding International Phonetic Alphabet (IPA) phonemes. The table below shows a mapping relationship between viseme IDs and mouth positions, listing typical IPA phonemes for each viseme ID.

-With temporal tags that are provided in a viseme event, these well-designed SVGs will be processed with smoothing modifications, and provide robust animation to the users. For example, the following illustration shows a red-lipped character that's designed for language learning.

-Each viseme event includes a series of frames in the `Animation` SDK property. These are grouped to best align the facial positions with the audio. Your 3D engine should render each group of `BlendShapes` frames immediately before the corresponding audio chunk. The `FrameIndex` value indicates how many frames preceded the current list of frames.

-The output json looks like the following sample. Each frame within `BlendShapes` contains an array of 55 facial positions represented as decimal values between 0 to 1. The decimal values are in the same order as described in the facial positions table below.

-The order of `BlendShapes` is as follows.

-# ms.devlang: cpp, csharp, golang, java, javascript, objective-c, python

-# ms.devlang: cpp, csharp, java, objective-c, python

-The Speech SDK is based on a native code base that's projected into multiple programming languages through a series of interoperability layers. Each language-specific projection has idiomatically correct features to manage the object lifecycle. Additionally, the Speech SDK includes memory management tooling to track resource usage with object logging and object limits.

-You have the option to create a warning threshold, and if that threshold is exceeded (assuming logging is enabled), a warning message is logged. The warning message contains a dump of all objects in existence along with their count. This information can be used to better understand issues.

-To enable a warning threshold, it must be specified on a `SpeechConfig` object. This object is checked when a new recognizer is created. In the following examples, let's assume that you've created an instance of `SpeechConfig` called `config`:

-Using the Speech SDK, you can set the maximum number of objects allowed at a given time. If this setting is enabled, when the maximum number is hit, attempts to create new recognizer objects will fail. Existing objects will continue to work.

-To enable an error threshold, it must be specified on a `SpeechConfig` object. This object is checked when a new recognizer is created. In the following examples, let's assume that you've created an instance of `SpeechConfig` called `config`:

-#

-Identify the format of the audio stream. The format must be supported by the Speech SDK and the Azure AI Speech service.

-# ms.devlang: cpp, csharp, golang, java, python

-In this guide, you use the Speech SDK to develop a console application that derives intents from speech utterances spoken through your device's microphone. You'll learn how to:

-# ms.devlang: cpp, csharp, golang, java, javascript, objective-c, python

-Logging to file is an optional feature for the Speech SDK. During development logging provides additional information and diagnostics from the Speech SDK's core components. It can be enabled by setting the property `Speech_LogFilename` on a speech configuration object to the location and name of the log file. Logging is handled by a static class in Speech SDKΓÇÖs native library. You can turn on logging for any Speech SDK recognizer or synthesizer instance. All instances in the same process write log entries to the same log file.

-The log file name is specified on a configuration object. Taking the `SpeechConfig` as an example and assuming that you've created an instance called `speechConfig`:

-You can create a recognizer from the configuration object. This will enable logging for all recognizers.

-For Windows or Linux, the log file can be in any path the user has write permission for. Write permissions to file system locations in other operating systems may be limited or restricted by default.

-You can save a log file to either internal storage, external storage, or the cache directory. Files created in the internal storage or the cache directory are private to the application. It is preferable to create a log file in external storage.

-The code above will save a log file to the external storage in the root of an application-specific directory. A user can access the file with the file manager (usually in `Android/data/ApplicationName/logfile.txt`). The file will be deleted when the application is uninstalled.

-In addition, you need to also set write permission in your Unity Player settings for Android to "External (SDCard)". The log will be written

-to a directory you can get using a tool such as AndroidStudio Device File Explorer. The exact directory path may vary between Android devices,

-location is typically the `sdcard/Android/data/your-app-packagename/files` directory.

-If you are using Objective-C on iOS, use the following code snippet to create a log file in the application document directory:

-If you're using Swift on iOS, please use the following code snippet to enable logs:

-Although a log file output path is specified as a configuration property into a `SpeechRecognizer` or other SDK object, SDK logging is a singleton, *process-wide* facility with no concept of individual instances. You can think of this as the `SpeechRecognizer` constructor (or similar) implicitly calling a static and internal "Configure Global Logging" routine with the property data available in the corresponding `SpeechConfig`.

-This means that you can't, as an example, configure six parallel recognizers to output simultaneously to six separate files. Instead, the latest recognizer created will configure the global logging instance to output to the file specified in its configuration properties and all SDK logging will be emitted to that file.

-This also means that the lifetime of the object that configured logging isn't tied to the duration of logging. Logging will not stop in response to the release of an SDK object and will continue as long as no new logging configuration is provided. Once started, process-wide logging may be stopped by setting the log file path to an empty string when creating a new object.

-To reduce potential confusion when configuring logging for multiple instances, it may be useful to abstract control of logging from objects doing real work. An example pair of helper routines:

-# ms.devlang: csharp, javascript

-You can transcribe meetings with the ability to add, remove, and identify multiple participants by streaming audio to the Speech service. You first create voice signatures for each participant using the REST API, and then use the voice signatures with the Speech SDK to transcribe meetings. See the Meeting Transcription [overview](meeting-transcription.md) for more information.

-> The Speech SDK for C++, Java, Objective-C, and Swift support Meeting Transcription, but we haven't yet included a guide here.

-> [Asynchronous Meeting Transcription](how-to-async-meeting-transcription.md)

-In this guide, you use the Speech SDK to develop a C++ console application that derives intents from user utterances through your device's microphone. You'll learn how to:

-All other special characters and punctuation will be ignored.

-Intents will be added using calls to the IntentRecognizer->AddIntent() API.

-To start developing a voice assistant for Windows, you'll need to make sure you have the proper development environment.

-For a complete voice assistant experience, the application will need a dialog service that

-Once you've received your Limited Access Feature token and bin file from Microsoft, you can begin on your own voice assistant on Windows.

-You can use phrase lists with both standard and [custom speech](custom-speech-overview.md). There are some situations where training a custom model that includes phrases is likely the best option to improve accuracy. For example, in the following cases you would use Custom Speech:

-You can use [Speech Studio](speech-studio-overview.md) to test how phrase list would help improve recognition for your audio. To implement a phrase list with your application in production, you'll use the Speech SDK or Speech CLI.

-"Hi Rehaan, this is Jessie from Contoso bank. "

-After testing, you might find that it's incorrectly recognized as:

-"Hi **everyone**, this is **Jesse** from **can't do so bank**."

-In this case you would want to add "Rehaan", "Jessie", and "Contoso" to your phrase list. Then the names should be recognized correctly.

-1. You test speech recognition by uploading an audio file or recording audio with a microphone. For example, select **record audio with a microphone** and then say "Hi Rehaan, this is Jessie from Contoso bank. " Then select the red button to stop recording.

-1. Enter "Contoso;Jessie;Rehaan" in the phrase list text box. Note that multiple phrases need to be separated by a semicolon.

-> [Custom Speech](custom-speech-overview.md)

-This sample shows how to use language detection with a custom endpoint. If the detected language is `en-US`, then the default model is used. If the detected language is `fr-FR`, then the custom model endpoint is used. For more information, see [Deploy a Custom Speech model](how-to-custom-speech-deploy-model.md).

-This sample shows how to use language detection with a custom endpoint. If the detected language is `en-US`, then the default model is used. If the detected language is `fr-FR`, then the custom model endpoint is used. For more information, see [Deploy a Custom Speech model](how-to-custom-speech-deploy-model.md).

-This sample shows how to use language detection with a custom endpoint. If the detected language is `en-US`, then the default model is used. If the detected language is `fr-FR`, then the custom model endpoint is used. For more information, see [Deploy a Custom Speech model](how-to-custom-speech-deploy-model.md).

-This sample shows how to use language detection with a custom endpoint. If the detected language is `en-US`, then the default model is used. If the detected language is `fr-FR`, then the custom model endpoint is used. For more information, see [Deploy a Custom Speech model](how-to-custom-speech-deploy-model.md).

-This sample shows how to use language detection with a custom endpoint. If the detected language is `en-US`, then the default model is used. If the detected language is `fr-FR`, then the custom model endpoint is used. For more information, see [Deploy a Custom Speech model](how-to-custom-speech-deploy-model.md).

-Additional remarks for Speech to text locales are included in the [Custom Speech](#custom-speech) section below.

-### Custom Speech

-To improve Speech to text recognition accuracy, customization is available for some languages and base models. Depending on the locale, you can upload audio + human-labeled transcripts, plain text, structured text, and pronunciation data. By default, plain text customization is supported for all available base models. To learn more about customization, see [Custom Speech](./custom-speech-overview.md).

-Audio and transcription logs can be used as input for [Custom Speech](custom-speech-overview.md) model training. You might have other use cases.

-This method is applicable for [Custom Speech](custom-speech-overview.md) endpoints only.

-1. Select **Custom Speech** > Your project name > **Deploy models**.

-The Speech to text REST API is used for [Batch transcription](batch-transcription.md) and [Custom Speech](custom-speech-overview.md). Changes from version 3.0 to 3.1 are described in the sections below.

-## Custom Speech

-The Speech to text REST API is used for [Batch transcription](batch-transcription.md) and [Custom Speech](custom-speech-overview.md). Changes from version 3.1 to 3.2 are described in the sections below.

-## Custom Speech

-¹ The region has dedicated hardware for Custom Speech training. If you plan to train a custom model with audio data, use one of the regions with dedicated hardware for faster training. Then you can [copy the trained model](how-to-custom-speech-train-model.md#copy-a-model) to another region.

-**Custom Speech**

-### Custom Speech

-Custom Speech service doesn't support automatic failover. We suggest the following steps to prepare for manual or automatic failover implemented in your client code. In these steps, you replicate custom models in a secondary region. With this preparation, your client code can switch to a secondary region when the primary region fails.

-3. Go to Speech Studio to load the copied model and create a new endpoint in the secondary region. See how to deploy a new model in [Deploy a Custom Speech model](./how-to-custom-speech-deploy-model.md).

-* [Batch transcription](batch-transcription.md) and [Custom Speech](custom-speech-overview.md) are not supported via REST API for short audio. You should always use the [Speech to text REST API](rest-speech-to-text.md) for batch transcription and Custom Speech.

-Speech to text REST API is used for [Batch transcription](batch-transcription.md) and [Custom Speech](custom-speech-overview.md).

-Datasets are applicable for [Custom Speech](custom-speech-overview.md). You can use datasets to train and test the performance of different models. For example, you can compare the performance of a model trained with a specific dataset to the performance of a model trained with a different dataset.

-Endpoints are applicable for [Custom Speech](custom-speech-overview.md). You must deploy a custom endpoint to use a Custom Speech model.

-Evaluations are applicable for [Custom Speech](custom-speech-overview.md). You can use evaluations to compare the performance of different models. For example, you can compare the performance of a model trained with a specific dataset to the performance of a model trained with a different dataset.

-See [Test recognition quality](how-to-custom-speech-inspect-data.md?pivots=rest-api) and [Test accuracy](how-to-custom-speech-evaluate-data.md?pivots=rest-api) for examples of how to test and evaluate Custom Speech models. This table includes all the operations that you can perform on evaluations.

-Models are applicable for [Custom Speech](custom-speech-overview.md) and [Batch Transcription](batch-transcription.md). You can use models to transcribe audio files. For example, you can use a model trained with a specific dataset to transcribe audio files.

-See [Train a model](how-to-custom-speech-train-model.md?pivots=rest-api) and [Custom Speech model lifecycle](how-to-custom-speech-model-and-endpoint-lifecycle.md?pivots=rest-api) for examples of how to train and manage Custom Speech models. This table includes all the operations that you can perform on models.

-Projects are applicable for [Custom Speech](custom-speech-overview.md). Custom Speech projects contain models, training and testing datasets, and deployment endpoints. Each project is specific to a [locale](language-support.md?tabs=stt). For example, you might create a project for English in the United States.

-Web hooks are applicable for [Custom Speech](custom-speech-overview.md) and [Batch Transcription](batch-transcription.md). In particular, web hooks apply to [datasets](#datasets), [endpoints](#endpoints), [evaluations](#evaluations), [models](#models), and [transcriptions](#transcriptions). Web hooks can be used to receive notifications about creation, processing, completion, and deletion events.

-You can manage access and permissions to your Speech resources with Azure role-based access control (Azure RBAC). Assigned roles can vary across Speech resources. For example, you can assign a role to a Speech resource that should only be used to train a Custom Speech model. You can assign another role to a Speech resource that is used to transcribe audio files. Depending on who can access each Speech resource, you can effectively set a different level of access per application or user. For more information on Azure RBAC, see the [Azure RBAC documentation](../../role-based-access-control/overview.md).

-For finer-grained resource access control, you can [add or remove roles](../../role-based-access-control/role-assignments-portal.md?tabs=current) using the Azure portal. For example, you could create a custom role with permission to upload Custom Speech datasets, but without permission to deploy a Custom Speech model to an endpoint.

-If Speech Studio uses your Microsoft Entra token, but the Speech resource doesn't have a custom subdomain and private endpoint, then you can't use some features in Speech Studio. In this case, for example, the Speech resource can be used to train a Custom Speech model, but you can't use a Custom Speech model to transcribe audio files.

-|Microsoft Entra token without custom subdomain and private endpoint (not recommended)|Features are limited. For example, the Speech resource can be used to train a Custom Speech model or custom neural voice. But you can't use a Custom Speech model or custom neural voice.|

-> The volume mount settings are only applicable for [Custom Speech to text](speech-container-cstt.md) containers.

-The Custom speech to text container transcribes real-time speech or batch audio recordings with intermediate results. You can use a custom model that you created in the [Custom Speech portal](https://speech.microsoft.com/customspeech). In this article, you'll learn how to download, install, and run a Custom speech to text container.

-The Custom speech to text container image for all supported versions and locales can be found on the [Microsoft Container Registry (MCR)](https://mcr.microsoft.com/product/azure-cognitive-services/speechservices/custom-speech-to-text/tags) syndicate. It resides within the `azure-cognitive-services/speechservices/` repository and is named `custom-speech-to-text`.

-The custom model has to have been [trained](how-to-custom-speech-train-model.md) by using the [Speech Studio](https://aka.ms/speechstudio/customspeech). For information about how to get the model ID, see [Custom Speech model lifecycle](how-to-custom-speech-model-and-endpoint-lifecycle.md).

-

-

-The Custom Speech to text container provides a default directory for writing the license file and billing log at runtime. The default directories are /license and /output respectively.

-| [Custom speech to text](speech-container-cstt.md) | Using a custom model from the [Custom Speech portal](https://speech.microsoft.com/customspeech), transcribes continuous real-time speech or batch audio recordings into text with intermediate results. | Latest: 4.3.0<br/><br/>For all supported versions and locales, see the [Microsoft Container Registry (MCR)](https://mcr.microsoft.com/product/azure-cognitive-services/speechservices/custom-speech-to-text/tags) and [JSON tags](https://mcr.microsoft.com/v2/azure-cognitive-services/speechservices/speech-to-text/tags/list). |

-The limits in this table apply per Speech resource when you create a Custom Speech model.

-1. Sign in if necessary, and go to **Custom Speech**.

-* [Custom Speech](https://aka.ms/speechstudio/customspeech): Create speech recognition models that are tailored to specific vocabulary sets and styles of speaking. In contrast to the base speech recognition model, Custom Speech models become part of your unique competitive advantage because they're not publicly accessible. To get started with uploading sample audio to create a Custom Speech model, see [Upload training and testing datasets](how-to-custom-speech-upload-data.md).

-## Custom Speech

-With [Custom Speech](./custom-speech-overview.md), you can evaluate and improve the accuracy of speech recognition for your applications and products. A custom speech model can be used for [real-time speech to text](speech-to-text.md), [speech translation](speech-translation.md), and [batch transcription](batch-transcription.md).

-> A [hosted deployment endpoint](how-to-custom-speech-deploy-model.md) isn't required to use Custom Speech with the [Batch transcription API](batch-transcription.md). You can conserve resources if the [custom speech model](how-to-custom-speech-train-model.md) is only used for batch transcription. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).

-A custom model can be used to augment the base model to improve recognition of domain-specific vocabulary specific to the application by providing text data to train the model. It can also be used to improve recognition based for the specific audio conditions of the application by providing audio data with reference transcriptions. For more information, see [Custom Speech](./custom-speech-overview.md) and [Speech to text REST API](rest-speech-to-text.md).

-Speech service offers a Swagger specification to interact with a handful of REST APIs used to import data, create models, test model accuracy, create custom endpoints, queue up batch transcriptions, and manage subscriptions. Most operations available through the [Custom Speech area of the Speech Studio](https://aka.ms/speechstudio/customspeech) can be completed programmatically using these APIs.

-* [Custom Speech](./custom-speech-overview.md)

-2. Optionally, to enable VPA on an existing cluster, use the `--enable-vpa` with the [az aks upgrade][az-aks-upgrade] command.

- az aks update -n myAKSCluster -g myResourceGroup --enable-vpa

-3. Optionally, to disable VPA on an existing cluster, use the `--disable-vpa` with the [az aks upgrade][az-aks-upgrade] command.

- 1. Enter a **Name** for your API center. It must be unique in your subscription.

-Azure Active Directory for authentication and role-based access control is available across regions that support Azure Cache for Redis.

-### Azure Active Directory-based authentication and authorization (preview)

-Azure Active Directory (Azure AD) based [authentication and authorization](cache-azure-active-directory-for-authentication.md) is now available for public preview with Azure Cache for Redis. With this Azure AD integration, users can connect to their cache instance without an access key and use [role-based access control](cache-configure-role-based-access-control.md) to connect to their cache instance.

-Azure Cache for Redis now supports authenticating storage account connections using managed identity. Identity is established through Azure Active Directory, and both system-assigned and user-assigned identities are supported. Support for managed identity further allows the service to establish trusted access to storage for uses including data persistence and importing/exporting cache data.

-Event-driven scaling automatically reduces capacity when demand for your functions is reduced. It does this by shutting down worker instances of your function app. Before an instance is shut down, new events stop being sent to the instance. Also, functions that are currently executing are given time to finish executing. This behavior is logged as drain mode. This shut-down period can extend up to 10 minutes for Consumption plan apps and up to 60 minutes for Premium plan apps. Event-driven scaling and this behavior don't apply to Dedicated plan apps.

- For example, if you set `description contains "this, that" (in the field there is no need to write the apostrophes), then the rule applies only to alerts whose description contains either `this` or `that`.

-* [Use autoscale actions to send email and webhook alert notifications](./autoscale-webhook-email.md)

-After you set up a diagnostic setting, data should start flowing to your selected destination(s) within 90 minutes. If you get no information within 24 hours, then you might be experiencing one of the following issues:

-| Red Hat Linux 8 | 8.5 | 4.18.0-348.\*el8_5.x86_644.18.0-348.\*el8.x86_64 |

-| | 8.3 | 4.18.0-240.\*el8_3.x86_64 |

-| CentOS Linux 8 | 8.5 | 4.18.0-348.\*el8_5.x86_644.18.0-348.\*el8.x86_64 |

-Administrative flags in NFSv4.x ACLs are special flags that are used only with Audit and Alarm ACL types. These flags define either success or failure access attempts for actions to be performed. For instance, if it's desired to audit failed access attempts to a specific file, then an administrative flag of ΓÇ£FΓÇ¥ can be used to control that behavior.

-This Audit ACL is an example of that, where `user1` is audited for failed access attempts for any permission level: `U:F:user1@contoso.com:rwatTnNcCy`.

-Azure NetApp Files only supports setting administrative flags for Audit ACEs. File access logging isn't currently supported. Alarm ACEs aren't supported in Azure NetApp Files.

- * The following example sets the **-BackupStorageRedundancy** option for the [Set-AzRecoveryServicesBackupProperty](/powershell/module/az.recoveryservices/set-azrecoveryservicesbackupproperty) cmd for **testvault** set to **GeoRedundant**.

-The following example sets the vault context for **testvault**.

-The command waits until the configure backup is completed and returns the following output.

-In the case of a SQL DB restore, the following restore scenarios are supported.

-Once the registered container is chosen, then we fetch the SQL instances within the container to which the DB should be restored to. Here we assume that there is 1 SQL instance within the "secondaryVM" and we fetch that instance.

-You can change the policy of the backed-up item from *Policy1* to *Policy2*. To switch policies for a backed-up item, fetch the relevant policy and back up item and use the [Enable-AzRecoveryServices](/powershell/module/az.recoveryservices/enable-azrecoveryservicesbackupprotection) command with backup item as the parameter.

-The command waits until the configure backup is completed and returns the following output.

-You can also create custom images from VMs running Docker on Windows.

-For Linux container workloads, Batch currently supports the following Linux images published by Microsoft Azure Batch in the Azure Marketplace without the need for a custom image.

-#### VM sizes without RDMA

- - Offer: `centos-container`

- - Offer: `ubuntu-server-container`

-#### VM sizes with RDMA

- - Offer: `centos-container-rdma`

- - Offer: `ubuntu-server-container-rdma`

- - Offer: `ubuntu-hpc`

-> It is recommended to use the `microsoft-dsvm` `ubuntu-hpc` VM image if possible.

- - For the HPC image, or `microsoft-dsvm` (Offer: `ubuntu-hpc`, etc.), the docker data root is unchanged from the Docker default which is _/var/lib/docker_ on Linux and _C:\ProgramData\Docker_ on Windows. These folders are located on the OS disk.

-You can also create custom images from VMs running Docker on one of the Linux distributions that's compatible with Batch. If you choose to provide your own custom Linux image, see the instructions in [Use a managed image to create a custom image pool](batch-custom-images.md).

-For Docker support on a custom image, install [Docker Pro](https://www.docker.com/products/pro/) or the open-source [Docker Community Edition](https://www.docker.com/community).

-Additional considerations for using a custom Linux image:

-Optional [ContainerRunOptions](/dotnet/api/microsoft.azure.batch.taskcontainersettings.containerrunoptions) are additional arguments you provide to the `docker create` command that Batch uses to create and run the container. For example, to set a working directory for the container, set the `--workdir <directory>` option. See the [docker create](https://docs.docker.com/engine/reference/commandline/create/) reference for additional options.

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-co-

-ms.co-

-1. After running your query, you will see the mapping between container registry and Azure DevOps pipeline. Click **...** next to the edge to see additional details on where the Azure DevOps pipeline was run.

-Below is an example of an advanced query that utilizes container image mapping. Starting with a Kubernetes workload that is exposed to the internet, you can trace all container images with high severity CVEs back to the Azure DevOps pipeline where the container image was built, empowering a security practitioner to kick off a developer remediation workflow.

-1. After running your query, you will see the mapping between container registry and GitHub workflow. Click **...** next to the edge to see additional details on where the GitHub workflow was run.

-Below is an example of an advanced query that utilizes container image mapping. Starting with a Kubernetes workload that is exposed to the internet, you can trace all container images with high severity CVEs back to the GitHub repository where the container image was built, empowering a security practitioner to kick off a developer remediation workflow.

-| December 13 | [Deny effect - replacing deprecated policies](#deny-effectreplacing-deprecated-policies) |

-### Deny effect - replacing deprecated policies

-December 13, 2023

-The [Deny effect](manage-mcsb.md#deny-and-enforce-recommendations) is used to prevent deployment of resources that don't comply with the [Microsoft Cloud Security Benchmark (MCSB) standard](concept-regulatory-compliance.md). A change in the policy's effects requires the deprecation of the current versions of the policy.

-To make sure you can still use the Deny effect, you must delete the old policies and assign the new policies in their place.

-**Deprecated policies**:

-Learn how to [Enable and configure at scale with an Azure built-in policy](defender-for-storage-policy-enablement.md).

-Check out [Azure Policy built-in definitions for Microsoft Defender for Cloud](policy-reference.md).

-description: In this quickstart, you use .NET Core to create an Azure Policy assignment to identify non-compliant resources.

-# Quickstart: Create a policy assignment to identify non-compliant resources with .NET Core

-The first step in understanding compliance in Azure is to identify the status of your resources. In

-this quickstart, you create a policy assignment to identify virtual machines that aren't using

-managed disks. When complete, you'll identify virtual machines that are _non-compliant_.

-The .NET Core library is used to manage Azure resources. This guide explains how to use the .NET

-Core library for Azure Policy to create a policy assignment.

-## Prerequisites

- [free](https://azure.microsoft.com/free/) account before you begin.

- service principal for use with Azure Policy or want to create a new one, see

- [Azure management libraries for .NET authentication](/dotnet/azure/sdk/authentication#mgmt-auth).

- Skip the step to install the .NET Core packages as we'll do that in the next steps.

-## Create the Azure Policy project

-To enable .NET Core to manage Azure Policy, create a new console application and install the

-required packages.

-1. Check that the latest .NET Core is installed (at least **3.1.8**). If it isn't yet installed,

- download it at [dotnet.microsoft.com](https://dotnet.microsoft.com/download/dotnet-core).

-1. Initialize a new .NET Core console application named "policyAssignment":

- ```dotnetcli

- dotnet new console --name "policyAssignment"

- ```

-1. Change directories into the new project folder and install the required packages for Azure

- Policy:

- ```dotnetcli

- # Add the Azure Policy package for .NET Core

- dotnet add package Microsoft.Azure.Management.ResourceManager --version 3.10.0-preview

- # Add the Azure app auth package for .NET Core

- dotnet add package Microsoft.Azure.Services.AppAuthentication --version 1.5.0

- ```

-1. Replace the default `program.cs` with the following code and save the updated file:

- ```csharp

- using System;

- using System.Collections.Generic;

- using System.Threading.Tasks;

- using Microsoft.IdentityModel.Clients.ActiveDirectory;

- using Microsoft.Rest;

- using Microsoft.Azure.Management.ResourceManager;

- using Microsoft.Azure.Management.ResourceManager.Models;

- namespace policyAssignment

- {

- class Program

- {

- static async Task Main(string[] args)

- {

- string strTenant = args[0];

- string strClientId = args[1];

- string strClientSecret = args[2];

- string strSubscriptionId = args[3];

- string strName = args[4];

- string strDisplayName = args[5];

- string strPolicyDefID = args[6];

- string strDescription = args[7];

- string strScope = args[8];

- var authContext = new AuthenticationContext($"https://login.microsoftonline.com/{strTenant}");

- var authResult = await authContext.AcquireTokenAsync(

- "https://management.core.windows.net",

- new ClientCredential(strClientId, strClientSecret));

- using (var client = new PolicyClient(new TokenCredentials(authResult.AccessToken)))

- {

- var policyAssignment = new PolicyAssignment

- {

- DisplayName = strDisplayName,

- PolicyDefinitionId = strPolicyDefID,

- Description = strDescription

- };

- var response = await client.PolicyAssignments.CreateAsync(strScope, strName, policyAssignment);

- }

- }

- }

- }

- ```

-1. Build and publish the `policyAssignment` console application:

- ```dotnetcli

- dotnet build

- dotnet publish -o {run-folder}

- ```

-## Create a policy assignment

-In this quickstart, you create a policy assignment and assign the **Audit VMs that do not use

-managed disks** (`06a78e20-9358-41c9-923c-fb736d382a4d`) definition. This policy definition

-identifies resources that aren't compliant to the conditions set in the policy definition.

-1. Change directories to the `{run-folder}` you defined with the previous `dotnet publish` command.

-1. Enter the following command in the terminal:

- ```bash

- policyAssignment.exe `

- "{tenantId}" `

- "{clientId}" `

- "{clientSecret}" `

- "{subscriptionId}" `

- "audit-vm-manageddisks" `

- "Audit VMs without managed disks Assignment" `

- "/providers/Microsoft.Authorization/policyDefinitions/06a78e20-9358-41c9-923c-fb736d382a4d" `

- "Shows all virtual machines not using managed disks" `

- "{scope}"

- ```

-The preceding commands use the following information:

- _audit-vm-manageddisks_.

- without managed disks Assignment_.

- assignment. In this case, it's the ID of policy definition _Audit VMs that do not use managed

- disks_.

- enforced on. It could range from a management group to an individual resource. Be sure to replace

- `{scope}` with one of the following patterns:

- - Management group: `/providers/Microsoft.Management/managementGroups/{managementGroup}`

- - Subscription: `/subscriptions/{subscriptionId}`

- - Resource group: `/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}`

- - Resource: `/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/[{parentResourcePath}/]`

-You're now ready to identify non-compliant resources to understand the compliance state of your

-environment.

-## Identify non-compliant resources

-Now that your policy assignment is created, you can identify resources that aren't compliant.

-1. Initialize a new .NET Core console application named "policyCompliance":

- ```dotnetcli

- dotnet new console --name "policyCompliance"

- ```

-1. Change directories into the new project folder and install the required packages for Azure

- Policy:

- ```dotnetcli

- # Add the Azure Policy package for .NET Core

- dotnet add package Microsoft.Azure.Management.PolicyInsights --version 3.1.0

- # Add the Azure app auth package for .NET Core

- dotnet add package Microsoft.Azure.Services.AppAuthentication --version 1.5.0

- ```

-1. Replace the default `program.cs` with the following code and save the updated file:

- ```csharp

- using System;

- using System.Collections.Generic;

- using System.Threading.Tasks;

- using Microsoft.IdentityModel.Clients.ActiveDirectory;

- using Microsoft.Rest;

- using Microsoft.Azure.Management.PolicyInsights;

- using Microsoft.Azure.Management.PolicyInsights.Models;

- namespace policyAssignment

- {

- class Program

- {

- static async Task Main(string[] args)

- {

- string strTenant = args[0];

- string strClientId = args[1];

- string strClientSecret = args[2];

- string strSubscriptionId = args[3];

- string strName = args[4];

- var authContext = new AuthenticationContext($"https://login.microsoftonline.com/{strTenant}");

- var authResult = await authContext.AcquireTokenAsync(

- "https://management.core.windows.net",

- new ClientCredential(strClientId, strClientSecret));

- using (var client = new PolicyInsightsClient(new TokenCredentials(authResult.AccessToken)))

- {

- var policyQueryOptions = new QueryOptions

- {

- Filter = $"IsCompliant eq false and PolicyAssignmentId eq '{strName}'",

- Apply = "groupby(ResourceId)"

- };

- var response = await client.PolicyStates.ListQueryResultsForSubscriptionAsync(

- "latest", strSubscriptionId, policyQueryOptions);

- Console.WriteLine(response.Odatacount);

- }

- }

- }

- }

- ```

-1. Build and publish the `policyAssignment` console application:

- ```dotnetcli

- dotnet build

- dotnet publish -o {run-folder}

- ```

-1. Change directories to the `{run-folder}` you defined with the previous `dotnet publish` command.

-1. Enter the following command in the terminal:

- ```bash

- policyCompliance.exe `

- "{tenantId}" `

- "{clientId}" `

- "{clientSecret}" `

- "{subscriptionId}" `

- "audit-vm-manageddisks"

- ```

-The preceding commands use the following information:

- _audit-vm-manageddisks_.

-The results in `response` match what you see in the **Resource compliance** tab of a policy

-assignment in the Azure portal view.

-## Clean up resources

- policy definition is a built-in, so there's no definition to remove.

- `policyAssignment` and `policyCompliance` project folders.

-## Next steps

-In this quickstart, you assigned a policy definition to identify non-compliant resources in your

-Azure environment.

-To learn more about assigning policy definitions to validate that new resources are compliant,

-continue to the tutorial for:

-> [!div class="nextstepaction"]

-> [Creating and managing policies](./tutorials/create-and-manage.md)

-description: In this quickstart, you use JavaScript to create an Azure Policy assignment to identify non-compliant resources.

-# Quickstart: Create a policy assignment to identify non-compliant resources using JavaScript

-The first step in understanding compliance in Azure is to identify the status of your resources. In

-this quickstart, you create a policy assignment to identify virtual machines that aren't using

-managed disks. When complete, you'll identify virtual machines that are _non-compliant_.

-The JavaScript library is used to manage Azure resources from the command line or in scripts. This

-guide explains how to use JavaScript library to create a policy assignment.

-## Prerequisites

- [free](https://azure.microsoft.com/free/) account before you begin.

-## Add the Policy libraries

-To enable JavaScript to work with Azure Policy, the libraries must be added. These libraries work

-wherever JavaScript can be used, including [bash on Windows 10](/windows/wsl/install-win10).

-1. Set up a new Node.js project by running the following command.

- ```bash

- npm init -y

- ```

-1. Add a reference to the yargs library.

- ```bash

- npm install yargs

- ```

-1. Add a reference to the Azure Policy libraries.

- ```bash

- # arm-policy is for working with Azure Policy objects such as definitions and assignments

- npm install @azure/arm-policy

- # arm-policyinsights is for working with Azure Policy compliance data such as events and states

- npm install @azure/arm-policyinsights

- ```

-1. Add a reference to the Azure authentication library.

- ```bash

- npm install @azure/identity

- ```

- > [!NOTE]

- > Verify in _package.json_ `@azure/arm-policy` is version **5.0.1** or higher,

- > `@azure/arm-policyinsights` is version **5.0.0** or higher, and `@azure/identity` is

- > version **2.0.4** or higher.

-## Create a policy assignment

-In this quickstart, you create a policy assignment and assign the **Audit VMs that do not use

-managed disks** (`06a78e20-9358-41c9-923c-fb736d382a4d`) definition. This policy definition

-identifies resources that aren't compliant to the conditions set in the policy definition.

-1. Create a new file named _policyAssignment.js_ and enter the following code.

- ```javascript

- const argv = require("yargs").argv;

- const { DefaultAzureCredential } = require("@azure/identity");

- const { PolicyClient } = require("@azure/arm-policy");

- if (argv.subID && argv.name && argv.displayName && argv.policyDefID && argv.scope && argv.description) {

- const createAssignment = async () => {

- const credentials = new DefaultAzureCredential();

- const client = new PolicyClient(credentials, argv.subID);

- const result = await client.policyAssignments.create(

- argv.scope,

- argv.name,

- {

- displayName: argv.displayName,

- policyDefinitionId: argv.policyDefID,

- description: argv.description

- }

- );

- console.log(result);

- };

- createAssignment();

- }

- ```

-1. Enter the following command in the terminal:

- ```bash

- node policyAssignment.js `

- --subID "{subscriptionId}" `

- --name "audit-vm-manageddisks" `

- --displayName "Audit VMs without managed disks Assignment" `

- --policyDefID "/providers/Microsoft.Authorization/policyDefinitions/06a78e20-9358-41c9-923c-fb736d382a4d" `

- --description "Shows all virtual machines not using managed disks" `

- --scope "{scope}"

- ```

-The preceding commands use the following information:

- with your subscription.

- _audit-vm-manageddisks_.

- without managed disks Assignment_.

- assignment. In this case, it's the ID of policy definition _Audit VMs that do not use managed

- disks_.

- enforced on. It could range from a management group to an individual resource. Be sure to replace

- `{scope}` with one of the following patterns:

- - Management group: `/providers/Microsoft.Management/managementGroups/{managementGroup}`

- - Subscription: `/subscriptions/{subscriptionId}`

- - Resource group: `/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}`

- - Resource: `/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/[{parentResourcePath}/]`

-You're now ready to identify non-compliant resources to understand the compliance state of your

-environment.

-## Identify non-compliant resources

-Now that your policy assignment is created, you can identify resources that aren't compliant.

-1. Create a new file named _policyState.js_ and enter the following code.

- ```javascript

- const argv = require("yargs").argv;

- const { DefaultAzureCredential } = require("@azure/identity");

- const { PolicyInsightsClient } = require("@azure/arm-policyinsights");

- if (argv.subID && argv.name) {

- const getStates = async () => {

- const credentials = new DefaultAzureCredential();

- const client = new PolicyInsightsClient(credentials);

- const result = client.policyStates.listQueryResultsForSubscription(

- "latest",

- argv.subID,

- {

- queryOptions: {

- filter: "IsCompliant eq false and PolicyAssignmentId eq '" + argv.name + "'",

- apply: "groupby((ResourceId))"

- }

- }

- );

- console.log(result);

- };

- getStates();

- }

- ```

-1. Enter the following command in the terminal:

- ```bash

- node policyState.js --subID "{subscriptionId}" --name "audit-vm-manageddisks"

- ```

-Replace `{subscriptionId}` with the subscription you want to see the compliance results for the

-policy assignment named 'audit-vm-manageddisks' that we created in the previous steps. For a list of

-other scopes and ways to summarize the data, see

-[PolicyStates*](/javascript/api/@azure/arm-policyinsights/) methods.

-Your results resemble the following example:

-```output

-{

- 'additional_properties': {

- '@odata.nextLink': None

- },

- 'odatacontext': 'https://management.azure.com/subscriptions/{subscriptionId}/providers/Microsoft.PolicyInsights/policyStates/$metadata#latest',

- 'odatacount': 12,

- 'value': [{data}]

-}

-```

-The results match what you see in the **Resource compliance** tab of a policy assignment in the

-Azure portal view.

-## Clean up resources

- policy definition is a built-in, so there's no definition to remove.

- ```bash

- npm uninstall @azure/arm-policy @azure/arm-policyinsights @azure/identity yargs

- ```

-## Next steps

-In this quickstart, you assigned a policy definition to identify non-compliant resources in your

-Azure environment.

-To learn more about assigning policy definitions to validate that new resources are compliant,

-continue to the tutorial for:

-> [!div class="nextstepaction"]

-> [Creating and managing policies](./tutorials/create-and-manage.md)

-description: In this quickstart, you use Python to create an Azure Policy assignment to identify non-compliant resources.

-# Quickstart: Create a policy assignment to identify non-compliant resources using Python

-The first step in understanding compliance in Azure is to identify the status of your resources. In

-this quickstart, you create a policy assignment to identify virtual machines that aren't using

-managed disks. When complete, you'll identify virtual machines that are _non-compliant_.

-The Python library is used to manage Azure resources from the command line or in scripts. This guide

-explains how to use Python library to create a policy assignment.

-## Prerequisites

-If you don't have an Azure subscription, create a [free](https://azure.microsoft.com/free/) account

-before you begin.

-## Add the Policy library

-To enable Python to work with Azure Policy, the library must be added. This library works wherever

-Python can be used, including [bash on Windows 10](/windows/wsl/install-win10) or locally installed.

-1. Check that the latest Python is installed (at least **3.8**). If it isn't yet installed, download

- it at [Python.org](https://www.python.org/downloads/).

-1. Check that the latest Azure CLI is installed (at least **2.5.1**). If it isn't yet installed, see

- [Install the Azure CLI](/cli/azure/install-azure-cli).

- > [!NOTE]

- > Azure CLI is required to enable Python to use the **CLI-based authentication** in the following

- > examples. For information about other options, see

- > [Authenticate using the Azure management libraries for Python](/azure/developer/python/sdk/authentication-overview).

-1. Authenticate through Azure CLI.

- ```azurecli

- az login

- ```

-1. In your Python environment of choice, install the required libraries for Azure Policy:

- ```bash

- # Add the Python library for Python

- pip install azure-mgmt-policyinsights

- # Add the Resources library for Python

- pip install azure-mgmt-resource

- # Add the CLI Core library for Python for authentication (development only!)

- pip install azure-cli-core

- # Add the Azure identity library for Python

- pip install azure.identity

- ```

- > [!NOTE]

- > If Python is installed for all users, these commands must be run from an elevated console.

-1. Validate that the libraries have been installed. `azure-mgmt-policyinsights` should be **0.5.0**

- or higher, `azure-mgmt-resource` should be **9.0.0** or higher, and `azure-cli-core` should be

- **2.5.0** or higher.

- ```bash

- # Check each installed library

- pip show azure-mgmt-policyinsights azure-mgmt-resource azure-cli-core azure.identity

- ```

-## Create a policy assignment

-In this quickstart, you create a policy assignment and assign the **Audit VMs that do not use

-managed disks** (`06a78e20-9358-41c9-923c-fb736d382a4d`) definition. This policy definition

-identifies resources that aren't compliant to the conditions set in the policy definition.

-Run the following code to create a new policy assignment:

-```python

-# Import specific methods and models from other libraries

-from azure.mgmt.resource.policy import PolicyClient

-from azure.mgmt.resource.policy.models import PolicyAssignment, Identity, UserAssignedIdentitiesValue, PolicyAssignmentUpdate

-from azure.identity import AzureCliCredential

-# Set subscription

-subId = "{subId}"

-assignmentLocation = "westus2"

-# Get your credentials from Azure CLI (development only!) and get your subscription list

-credential = AzureCliCredential()

-policyClient = PolicyClient(credential, subId, base_url=none)

-# Create details for the assignment

-policyAssignmentIdentity = Identity(type="SystemAssigned")

-policyAssignmentDetails = PolicyAssignment(display_name="Audit VMs without managed disks Assignment", policy_definition_id="/providers/Microsoft.Authorization/policyDefinitions/06a78e20-9358-41c9-923c-fb736d382a4d", description="Shows all virtual machines not using managed disks", identity=policyAssignmentIdentity, location=assignmentLocation)

-# Create new policy assignment

-policyAssignment = policyClient.policy_assignments.create("{scope}", "audit-vm-manageddisks", policyAssignmentDetails)

-# Show results

-print(policyAssignment)

-```

-The preceding commands use the following information:

-Assignment details:

- subscription.

- without managed disks Assignment_.

- assignment. In this case, it's the ID of policy definition _Audit VMs that do not use managed

- disks_. In this example, the policy definition is a built-in and the path doesn't include

- management group or subscription information.

- enforced on. It could range from a management group to an individual resource. Be sure to replace

- `{scope}` with one of the following patterns:

- - Management group: `/providers/Microsoft.Management/managementGroups/{managementGroup}`

- - Subscription: `/subscriptions/{subscriptionId}`

- - Resource group: `/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}`

- - Resource: `/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/[{parentResourcePath}/]`

-Assignment creation:

- assignment details must exist within this scope.

-You're now ready to identify non-compliant resources to understand the compliance state of your

-environment.

-## Identify non-compliant resources

-Use the following information to identify resources that aren't compliant with the policy assignment

-you created. Run the following code:

-```python

-# Import specific methods and models from other libraries

-from azure.mgmt.policyinsights._policy_insights_client import PolicyInsightsClient

-from azure.mgmt.policyinsights.models import QueryOptions

-from azure.identity import AzureCliCredential

-# Set subscription

-subId = "{subId}"

-# Get your credentials from Azure CLI (development only!) and get your subscription list

-credential = AzureCliCredential()

-policyClient = PolicyInsightsClient(credential, subId, base_url=none)

-# Set the query options

-queryOptions = QueryOptions(filter="IsCompliant eq false and PolicyAssignmentId eq 'audit-vm-manageddisks'",apply="groupby((ResourceId))")

-# Fetch 'latest' results for the subscription

-results = policyInsightsClient.policy_states.list_query_results_for_subscription(policy_states_resource="latest", subscription_id=subId, query_options=queryOptions)

-# Show results

-print(results)

-```

-Replace `{subId}` with the subscription you want to see the compliance results for this policy

-assignment. For a list of other scopes and ways to summarize the data, see

-[Policy State methods](/python/api/azure-mgmt-policyinsights/azure.mgmt.policyinsights.operations.policystatesoperations#methods).

-Your results resemble the following example:

-```output

-{

- 'additional_properties': {

- '@odata.nextLink': None

- },

- 'odatacontext': 'https://management.azure.com/subscriptions/{subscriptionId}/providers/Microsoft.PolicyInsights/policyStates/$metadata#latest',

- 'odatacount': 12,

- 'value': [{data}]

-}

-```

-The results match what you see in the **Resource compliance** tab of a policy assignment in the

-Azure portal view.

-## Clean up resources

-To remove the assignment created, use the following command:

-```python

-# Import specific methods and models from other libraries

-from azure.mgmt.resource.policy import PolicyClient

-from azure.identity import AzureCliCredential

-# Set subscription

-subId = "{subId}"

-# Get your credentials from Azure CLI (development only!) and get your subscription list

-credential = AzureCliCredential()

-policyClient = PolicyClient(credential, subId, base_url=none)

-# Delete the policy assignment

-policyAssignment = policyClient.policy_assignments.delete("{scope}", "audit-vm-manageddisks")

-# Show results

-print(policyAssignment)

-```

-Replace `{subId}` with your subscription and `{scope}` with the same scope you used to create the

-policy assignment.

-## Next steps

-In this quickstart, you assigned a policy definition to identify non-compliant resources in your

-Azure environment.

-To learn more about assigning policy definitions to validate that new resources are compliant,

-continue to the tutorial for:

-> [!div class="nextstepaction"]

-> [Creating and managing policies](./tutorials/create-and-manage.md)

-Here are some examples of setting **resultFormat** to use the _ObjectArray_ format:

-```csharp

-var requestOptions = new QueryRequestOptions( resultFormat: ResultFormat.ObjectArray);

-var request = new QueryRequest(subscriptions, "Resources | limit 1", options: requestOptions);

-```

-```python

-request_options = QueryRequestOptions(

- result_format=ResultFormat.object_array

-)

-request = QueryRequest(query="Resources | limit 1", subscriptions=subs_list, options=request_options)

-response = client.resources(request)

-```

-Azure Resource Graph Explorer, part of Azure portal, enables running Resource Graph queries directly

-in the Azure portal. Pin the results as dynamic charts to provide real-time dynamic information to

-your portal workflow. For more information, see

-[First query with Azure Resource Graph Explorer](./first-query-portal.md).

-Resource Graph supports Azure CLI, Azure PowerShell, Azure SDK for Python, and more. The query is

-structured the same for each language. Learn how to enable Resource Graph with:

-keywords: hadoop high availability

-keywords: hadoop high availability

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-keywords:

-| `playbook_04_00_01_hana_hsr.yaml` | SAP HANA high-availability configuration |

-kv_name: DEVWEEUSAP01user###

-secret_prefix: DEV-WEEU-SAP01

-sap_sid: X01

- - { host: 'x01dxdb00l0538', LUN: 0, type: 'sap' }

- - { host: 'x01dxdb00l0538', LUN: 10, type: 'data' }

- - { host: 'x01dxdb00l0538', LUN: 11, type: 'data' }

- - { host: 'x01dxdb00l0538', LUN: 12, type: 'data' }

- - { host: 'x01dxdb00l0538', LUN: 13, type: 'data' }

- - { host: 'x01dxdb00l0538', LUN: 20, type: 'log' }

- - { host: 'x01dxdb00l0538', LUN: 21, type: 'log' }

- - { host: 'x01dxdb00l0538', LUN: 22, type: 'log' }

- - { host: 'x01dxdb00l0538', LUN: 2, type: 'backup' }

- - { host: 'x01app00l538', LUN: 0, type: 'sap' }

- - { host: 'x01app01l538', LUN: 0, type: 'sap' }

- - { host: 'x01scs00l538', LUN: 0, type: 'sap' }

-The `X01_hosts.yaml` file is the inventory file that Ansible uses for configuration of the SAP infrastructure. The `X01` label might differ for your deployments.

-X01_DB:

- x01dxdb00l0538:

-X01_SCS:

- x01scs00l538:

-X01_ERS:

-X01_PAS:

- x01app00l538:

-X01_APP:

- x01app01l538:

-X01_WEB:

-password_secret=$(az keyvault secret show --vault-name ${kv_name} --name ${password_secret_name} | jq -r .value)

-# Ref: https://docs.ansible.com/ansible/2.9/reference_appendices/interpreter_discovery.html

-# Silence warnings about Python interpreter discovery

-### Operating system configuration

-### SAP-specific operating system configuration

-### Local software download

- location = "westeurope"

- location = "westeurope"

-For example, choose **West Europe** as the deployment location, with the four-character name `WEEU`, as previously described. The sample deployer configuration file `LAB-WEEU-DEP05-INFRASTRUCTURE.tfvars` is in the `${HOME}/Azure_SAP_Automated_Deployment/WORKSPACES/DEPLOYER/LAB-WEEU-DEP05-INFRASTRUCTURE` folder.

-The sample SAP library configuration file `LAB-WEEU-SAP_LIBRARY.tfvars` is in the `${HOME}/Azure_SAP_Automated_Deployment/WORKSPACES/LIBRARY/LAB-WEEU-SAP_LIBRARY` folder.

-export region_code="WEEU"

-# - Key Vault: LABWEEUDEP05user39B #

-# - Storage Account: mgmtnoeutfstate53e #

-# - Web Application Name: mgmt-noeu-sapdeployment39B #

-terraform_state_storage_account=labweeutfstate###

-export region_code="WEEU"

-terraform_state_storage_account=labweeutfstate###

- vault_name="LABWEEUDEP05user###"

-export region_code="WEEU"

- - Following from the preceding example, the resource group is `LAB-WEEU-SAP_LIBRARY`.

- - The name of the storage account contains `mgmtnoeutfstate`.

- - Following from the preceding example, the resource group is `LAB-WEEU-DEP05-INFRASTRUCTURE`.

- - The name of the key vault contains `LABWEEUDEP05user`.

- - Following from the preceding example, the name of the blob is `LAB-WEEU-DEP05-INFRASTRUCTURE.terraform.tfstate`.

- export region_code="WEEU"

- cd ${HOME}/Azure_SAP_Automated_Deployment/WORKSPACES/LANDSCAPE/LAB-WEEU-SAP04-INFRASTRUCTURE

-export region_code="WEEU"

-export region_code="WEEU"

-For this example configuration, the resource group is `LAB-WEEU-DEP05-INFRASTRUCTURE`. The deployer key vault name contains `LABWEEUDEP05user` in the name. You use this information to configure your deployer's key vault secrets.

-cd ${HOME}/Azure_SAP_Automated_Deployment/WORKSPACES/SYSTEM/LAB-WEEU-SAP04-L00/

-Go to the `DEV-WEEU-SAP01-X00` subfolder inside the `SYSTEM` folder. Then, run this command:

-export sap_env_code="DEV"

-export region_code="WEEU"

-cd ${HOME}/Azure_SAP_Automated_Deployment/WORKSPACES/SYSTEM/${sap_env_code}-${region_code}-${sap_vnet_code}-X00

- --parameterfile "${sap_env_code}-${region_code}-${sap_vnet_code}-X00.tfvars" \

-Go to the `DEV-XXXX-SAP01-INFRASTRUCTURE` subfolder inside the `LANDSCAPE` folder. Then, run the following command:

-export sap_env_code="DEV"

-export region_code="WEEU"

-export region_code="WEEU"

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-*SAPCONT_ADMIN@SAPCONTOSO.local* has Domain Administrator rights and is used to run *SAPinst*, *\<sid>adm*, and *SAPService\<SID>* as SAP system users and the *SAP_\<SAPSID>_GlobalAdmin* group. The SAP Installation Guide contains the specific details required for these accounts.

- 1. If necessary, add a DNS A record into Windows DNS for *<storage_account_name>.file.core.windows.net*. (This might need to be in a new DNS zone.) Discuss this topic with the DNS administrator. The new zone should not update outside an organization.

- * The user who's running the script must have permission to create objects in the Active Directory domain that contains the SAP servers. Typically, an organization uses a Domain Administrator account such as *SAPCONT_ADMIN@SAPCONTOSO.local*.

- * Before the user runs the script, confirm that this Active Directory domain user account is synchronized with Microsoft Entra ID. An example of this would be to open the Azure portal and go to Microsoft Entra users, check that the user *SAPCONT_ADMIN@SAPCONTOSO.local* exists, and verify the Microsoft Entra user account.

- * Grant the Contributor role-based access control (RBAC) role to this Microsoft Entra user account for the resource group that contains the storage account that holds the file share. In this example, the user *SAPCONT_ADMIN@SAPCONTOSO.onmicrosoft.com* is granted the Contributor role to the respective resource group.

- In this example scenario, the Active Directory administrator would log on to the Windows Server instance as *SAPCONT_ADMIN@SAPCONTOSO.local*. When the administrator is using the PowerShell command `Connect-AzAccount`, the administrator connects as user *SAPCONT_ADMIN@SAPCONTOSO.onmicrosoft.com*. Ideally, the Active Directory administrator and the Azure administrator should work together on this task.

-1. [Install the Windows cluster on ASCS/ERS nodes and add the cloud witness](sap-high-availability-infrastructure-wsfc-shared-disk.md#0d67f090-7928-43e0-8772-5ccbf8f59aab).

-2. The first cluster node installation asks for the Azure Files SMB storage account name. Enter the FQDN *<storage_account_name>.file.core.windows.net*. If SAPinst doesn't accept more than 13 characters, the SWPM version is too old.

-keywords: ''

-keywords: ''

-[anf-avail-matrix]:https://azure.microsoft.com/global-infrastructure/services/?products=storage&regions=all

-[2205917]:https://launchpad.support.sap.com/#/notes/2205917

-[1944799]:https://launchpad.support.sap.com/#/notes/1944799

-[2191498]:https://launchpad.support.sap.com/#/notes/2191498

-[2243692]:https://launchpad.support.sap.com/#/notes/2243692

-[1984787]:https://launchpad.support.sap.com/#/notes/1984787

-[1410736]:https://launchpad.support.sap.com/#/notes/1410736

-[sap-swcenter]:https://support.sap.com/en/my-support/software-downloads.html

-[suse-ha-guide]:https://www.suse.com/products/sles-for-sap/resource-library/sap-best-practices/

-[suse-drbd-guide]:https://www.suse.com/documentation/sle-ha-12/singlehtml/book_sleha_techguides/book_sleha_techguides.html

-[suse-ha-12sp3-relnotes]:https://www.suse.com/releasenotes/x86_64/SLE-HA/12-SP3/

-[template-multisid-xscs]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-xscs-md%2Fazuredeploy.json

-[template-converged]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-converged-md%2Fazuredeploy.json

-[template-file-server]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-file-server-md%2Fazuredeploy.json

-[nfs-ha]:high-availability-guide-suse-nfs.md

-1. [Set the ASCS/SCS load balancing rules for the Azure internal load balancer](./sap-high-availability-infrastructure-wsfc-shared-disk.md#fe0bd8b5-2b43-45e3-8295-80bee5415716).

-2. [Add Windows virtual machines to the domain](./sap-high-availability-infrastructure-wsfc-shared-disk.md#e69e9a34-4601-47a3-a41c-d2e11c626c0c).

-3. [Add registry entries on both cluster nodes of the SAP ASCS/SCS instance](./sap-high-availability-infrastructure-wsfc-shared-disk.md#661035b2-4d0f-4d31-86f8-dc0a50d78158)

-4. [Set up a Windows Server failover cluster for an SAP ASCS/SCS instance](./sap-high-availability-infrastructure-wsfc-shared-disk.md#0d67f090-7928-43e0-8772-5ccbf8f59aab)

-Set `KeepAlive` parameters on both SAP ASCS/SCS cluster nodes follow the instructions to [Set registry entries on the cluster nodes of the SAP ASCS/SCS instance](./sap-high-availability-infrastructure-wsfc-shared-disk.md#661035b2-4d0f-4d31-86f8-dc0a50d78158).

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-tags: azure-resource-manager

-keywords: ''

-Both ERS1 and ERS2 are supported in a multi-SID configuration. A mix of ERS1 and ERS2 is not supported in the same cluster.

-You'll install a new SAP SID PR2 instance, in addition to the existing clustered SAP PR1 ASCS/SCS instance.

-SAP ASCS, SAP SCS, and SAP ERS2 use virtual host names and virtual IP addresses. On Azure, a [load balancer](../../load-balancer/load-balancer-overview.md) is required to use a virtual IP address.

-We strongly recommend using a [standard load balancer](../../load-balancer/quickstart-load-balancer-standard-public-portal.md).

-You need to add configuration to the existing load balancer for the second SAP SID instance, PR2, for ASCS, SCS, or ERS. The configuration for the first SAP SID, PR1, should be already in place.

-#### (A)SCS PR2 [instance number 02]

- - Static ASCS/SCS IP address 10.0.0.45.

- - Already in place. The VMs were added to the back-end pool during configuration of SAP SID PR1.

- - Port 620*nr* [62002]. Leave the default options for protocol (TCP), interval (5), and unhealthy threshold (2).

- - If you're using a standard load balancer, select high-availability (HA) ports.

- - If you're using a basic load balancer, create load-balancing rules for the following ports:

- - 32*nr* TCP [3202]

- - 36*nr* TCP [3602]

- - 39*nr* TCP [3902]

- - 81*nr* TCP [8102]

- - 5*nr*13 TCP [50213]

- - 5*nr*14 TCP [50214]

- - 5*nr*16 TCP [50216]

- - Associate load-balancing rules with the PR2 ASCS front-end IP address, the health probe, and the existing back-end pool.

- - Make sure that idle timeout is set to the maximum value of 30 minutes, and that floating IP (direct server return) is enabled.

-#### ERS2 PR2 [instance number 12]

-Because ERS2 is clustered, you must configure the ERS2 virtual IP address on an Azure internal load balancer in addition to the preceding SAP ASCS/SCS IP address. This section applies only if you're using the ERS2 architecture for PR2.

- - Static SAP ERS2 IP address 10.0.0.46.

- - The VMs were already added to the internal load balancer's back-end pool.

- - Port 621*nr* [62112]. Leave the default options for protocol (TCP), interval (5), and unhealthy threshold (2).

- - If you're using a standard load balancer, select HA ports.

- - If you're using a basic load balancer, create load-balancing rules for the following ports:

- - 32*nr* TCP [3212]

- - 33*nr* TCP [3312]

- - 5*nr*13 TCP [51212]

- - 5*nr*14 TCP [51212]

- - 5*nr*16 TCP [51212]

-

- - Associate load-balancing rules with the PR2 ERS2 front-end IP address, the health probe, and the existing back-end pool.

- - Make sure that idle timeout is set to the maximum value of 30 minutes, and that floating IP (direct server return) is enabled.

- The IP address that you assign to the virtual host name in DNS must be the same as the IP address that you assigned in Azure Load Balancer.

- The IP address that you assign to the virtual host name for ERS2 in DNS must be the same as the IP address that you assigned in Azure Load Balancer.

-3. To define the IP address that's assigned to the virtual host name, select **DNS Manager** > **Domain**.

-If you're running ERS1, add the SAP profile parameter `enque/encni/set_so_keepalive`. The profile parameter prevents connections between SAP work processes and the enqueue server from closing when they're idle for too long. The SAP parameter is not required for ERS2.

-1. Install the database instance by following the process that's described in the SAP installation guide.

-3. Install the SAP Primary Application Server (PAS) instance on the virtual machine that you've designated to host the PAS.

-keywords: ''

-8. Install the SAP primary application server on the new dedicated VM, as described in the SAP installation guide.

-9. Install the SAP additional application server on the new dedicated VM, as described in the SAP installation guide.

-10. [Test the SAP ASCS/SCS instance failover and SIOS replication][sap-high-availability-installation-wsfc-shared-disk-test-ascs-failover-and-sios-repl].

-[1928533]:https://launchpad.support.sap.com/#/notes/1928533

-[1999351]:https://launchpad.support.sap.com/#/notes/1999351

-[2015553]:https://launchpad.support.sap.com/#/notes/2015553

-[2178632]:https://launchpad.support.sap.com/#/notes/2178632

-[2243692]:https://launchpad.support.sap.com/#/notes/2243692

-[1869038]:https://launchpad.support.sap.com/#/notes/1869038

-[2287140]:https://launchpad.support.sap.com/#/notes/2287140

-[2492395]:https://launchpad.support.sap.com/#/notes/2492395

-[sap-installation-guides]:http://service.sap.com/instguides

-[azure-resource-manager/management/azure-subscription-service-limits]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[azure-resource-manager/management/azure-subscription-service-limits-subscription]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[sap-high-availability-architecture-scenarios]:sap-high-availability-architecture-scenarios.md

-[sap-high-availability-guide-wsfc-shared-disk]:sap-high-availability-guide-wsfc-shared-disk.md

-[sap-high-availability-guide-wsfc-file-share]:sap-high-availability-guide-wsfc-file-share.md

-[sap-ascs-high-availability-multi-sid-wsfc]:sap-ascs-high-availability-multi-sid-wsfc.md

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-hana-ha]:sap-hana-high-availability.md

-[sap-suse-ascs-ha]:high-availability-guide-suse.md

-[sap-net-weaver-ports-ascs-scs-ports]:sap-high-availability-infrastructure-wsfc-shared-disk.md#fe0bd8b5-2b43-45e3-8295-80bee5415716

-[dbms-guide]:../../virtual-machines-windows-sap-dbms-guide-general.md

-[deployment-guide]:deployment-guide.md

-[dr-guide-classic]:https://go.microsoft.com/fwlink/?LinkID=521971

-[getting-started]:get-started.md

-[planning-guide]:planning-guide.md

-[planning-guide-11]:planning-guide.md

-[planning-guide-2.1]:planning-guide.md#1625df66-4cc6-4d60-9202-de8a0b77f803

-[planning-guide-2.2]:planning-guide.md#f5b3b18c-302c-4bd8-9ab2-c388f1ab3d10

-[planning-guide-microsoft-azure-networking]:planning-guide.md#61678387-8868-435d-9f8c-450b2424f5bd

-[planning-guide-storage-microsoft-azure-storage-and-data-disks]:planning-guide.md#a72afa26-4bf4-4a25-8cf7-855d6032157f

-[sap-high-availability-architecture-scenarios]:sap-high-availability-architecture-scenarios.md

-[sap-high-availability-guide-wsfc-shared-disk]:sap-high-availability-guide-wsfc-shared-disk.md

-[sap-high-availability-guide-wsfc-file-share]:sap-high-availability-guide-wsfc-file-share.md

-[sap-ascs-high-availability-multi-sid-wsfc]:sap-ascs-high-availability-multi-sid-wsfc.md

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-high-availability-infrastructure-wsfc-file-share]:sap-high-availability-infrastructure-wsfc-file-share.md

-[sap-high-availability-installation-wsfc-shared-disk]:sap-high-availability-installation-wsfc-shared-disk.md

-[sap-high-availability-installation-wsfc-shared-disk-change-ers-service-startup-type]:sap-high-availability-installation-wsfc-shared-disk.md#094bc895-31d4-4471-91cc-1513b64e406a

-[sap-high-availability-installation-wsfc-file-share]:sap-high-availability-installation-wsfc-file-share.md

-[sap-high-availability-infrastructure-wsfc-shared-disk-install-sios]:sap-high-availability-infrastructure-wsfc-shared-disk.md#5c8e5482-841e-45e1-a89d-a05c0907c868

-[Logo_Linux]:media/virtual-machines-shared-sap-shared/Linux.png

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-[virtual-machines-azure-resource-manager-architecture-benefits-arm]:../../azure-resource-manager/management/overview.md#the-benefits-of-using-resource-manager

-[virtual-machines-manage-availability]:../../virtual-machines/availability.md

-keywords: ''

-keywords: ''

-keywords: ''

-[1928533]:https://launchpad.support.sap.com/#/notes/1928533

-[1999351]:https://launchpad.support.sap.com/#/notes/1999351

-[2015553]:https://launchpad.support.sap.com/#/notes/2015553

-[2178632]:https://launchpad.support.sap.com/#/notes/2178632

-[2243692]:https://launchpad.support.sap.com/#/notes/2243692

-[sap-installation-guides]:http://service.sap.com/instguides

-[azure-resource-manager/management/azure-subscription-service-limits]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[azure-resource-manager/management/azure-subscription-service-limits-subscription]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[dbms-guide]:../../virtual-machines-windows-sap-dbms-guide-general.md

-[deployment-guide]:deployment-guide.md

-[dr-guide-classic]:https://go.microsoft.com/fwlink/?LinkID=521971

-[getting-started]:get-started.md

-[sap-high-availability-architecture-scenarios]:sap-high-availability-architecture-scenarios.md

-[sap-high-availability-guide-wsfc-shared-disk]:sap-high-availability-guide-wsfc-shared-disk.md

-[sap-ascs-high-availability-multi-sid-wsfc]:sap-ascs-high-availability-multi-sid-wsfc.md

-[sap-high-availability-infrastructure-wsfc-shared-disk-default-ascs-ilb-rules]:sap-high-availability-infrastructure-wsfc-shared-disk.md#fe0bd8b5-2b43-45e3-8295-80bee5415716

-[sap-high-availability-infrastructure-wsfc-shared-disk-change-ascs-ilb-rules]:sap-high-availability-infrastructure-wsfc-shared-disk.md#fe0bd8b5-2b43-45e3-8295-80bee5415716

-[sap-high-availability-infrastructure-wsfc-shared-disk-add-win-domain]:sap-high-availability-infrastructure-wsfc-shared-disk.md#e69e9a34-4601-47a3-a41c-d2e11c626c0c

-[sap-hana-ha]:sap-hana-high-availability.md

-[sap-suse-ascs-ha]:high-availability-guide-suse.md

-[planning-guide]:planning-guide.md

-[planning-guide-11]:planning-guide.md

-[planning-guide-2.1]:planning-guide.md#1625df66-4cc6-4d60-9202-de8a0b77f803

-[planning-guide-2.2]:planning-guide.md#f5b3b18c-302c-4bd8-9ab2-c388f1ab3d10

-[planning-guide-microsoft-azure-networking]:planning-guide.md#61678387-8868-435d-9f8c-450b2424f5bd

-[planning-guide-storage-microsoft-azure-storage-and-data-disks]:planning-guide.md#a72afa26-4bf4-4a25-8cf7-855d6032157f

-[sap-high-availability-infrastructure-wsfc-file-share]:sap-high-availability-infrastructure-wsfc-file-share.md

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-ha-guide-2]:#42b8f600-7ba3-4606-b8a5-53c4f026da08

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-[sap-ha-guide-figure-3024]:./media/virtual-machines-shared-sap-high-availability-guide/3024-selection-screen-different-quorum-configurations.png

-[sap-ha-guide-figure-3025]:./media/virtual-machines-shared-sap-high-availability-guide/3025-selection-screen-file-share-witness.png

-[sap-ha-guide-figure-3026]:./media/virtual-machines-shared-sap-high-availability-guide/3026-define-file-share-location-for-witness-share.png

-[sap-ha-guide-figure-3027]:./media/virtual-machines-shared-sap-high-availability-guide/3027-successful-reconfiguration-cluster-file-share-witness.png

-[sap-ha-guide-figure-3028]:./media/virtual-machines-shared-sap-high-availability-guide/3028-install-dot-net-framework-35.png

-[sap-ha-guide-figure-3029]:./media/virtual-machines-shared-sap-high-availability-guide/3029-install-dot-net-framework-35-progress.png

-[sap-ha-guide-figure-3030]:./media/virtual-machines-shared-sap-high-availability-guide/3030-sios-installer.png

-[sap-ha-guide-figure-3031]:./media/virtual-machines-shared-sap-high-availability-guide/3031-first-screen-sios-data-keeper-installation.png

-[sap-ha-guide-figure-3032]:./media/virtual-machines-shared-sap-high-availability-guide/3032-data-keeper-informs-service-be-disabled.png

-[sap-ha-guide-figure-3033]:./media/virtual-machines-shared-sap-high-availability-guide/3033-user-selection-sios-data-keeper.png

-[sap-ha-guide-figure-3034]:./media/virtual-machines-shared-sap-high-availability-guide/3034-domain-user-sios-data-keeper.png

-[sap-ha-guide-figure-3035]:./media/virtual-machines-shared-sap-high-availability-guide/3035-provide-sios-data-keeper-license.png

-[sap-ha-guide-figure-3036]:./media/virtual-machines-shared-sap-high-availability-guide/3036-data-keeper-management-config-tool.png

-[sap-ha-guide-figure-3037]:./media/virtual-machines-shared-sap-high-availability-guide/3037-tcp-ip-address-first-node-data-keeper.png

-[sap-ha-guide-figure-3038]:./media/virtual-machines-shared-sap-high-availability-guide/3038-create-replication-sios-job.png

-[sap-ha-guide-figure-3039]:./media/virtual-machines-shared-sap-high-availability-guide/3039-define-sios-replication-job-name.png

-[sap-ha-guide-figure-3040]:./media/virtual-machines-shared-sap-high-availability-guide/3040-define-sios-source-node.png

-[sap-ha-guide-figure-3041]:./media/virtual-machines-shared-sap-high-availability-guide/3041-define-sios-target-node.png

-[sap-ha-guide-figure-3042]:./media/virtual-machines-shared-sap-high-availability-guide/3042-define-sios-synchronous-replication.png

-[sap-ha-guide-figure-3043]:./media/virtual-machines-shared-sap-high-availability-guide/3043-enable-sios-replicated-volume-as-cluster-volume.png

-[sap-ha-guide-figure-3044]:./media/virtual-machines-shared-sap-high-availability-guide/3044-data-keeper-synchronous-mirroring-for-SAP-gui.png

-[sap-ha-guide-figure-3045]:./media/virtual-machines-shared-sap-high-availability-guide/3045-replicated-disk-by-data-keeper-in-wsfc.png

-[sap-ha-guide-figure-3046]:./media/virtual-machines-shared-sap-high-availability-guide/3046-dns-entry-sap-ascs-virtual-name-ip.png

-[sap-ha-guide-figure-3047]:./media/virtual-machines-shared-sap-high-availability-guide/3047-dns-manager.png

-[sap-ha-guide-figure-3048]:./media/virtual-machines-shared-sap-high-availability-guide/3048-default-cluster-probe-port.png

-[sap-ha-guide-figure-3049]:./media/virtual-machines-shared-sap-high-availability-guide/3049-cluster-probe-port-after.png

-[sap-ha-guide-figure-3050]:./media/virtual-machines-shared-sap-high-availability-guide/3050-service-type-ers-delayed-automatic.png

-[sap-ha-guide-figure-5000]:./media/virtual-machines-shared-sap-high-availability-guide/5000-wsfc-sap-sid-node-a.png

-[sap-ha-guide-figure-5001]:./media/virtual-machines-shared-sap-high-availability-guide/5001-sios-replicating-local-volume.png

-[sap-ha-guide-figure-5002]:./media/virtual-machines-shared-sap-high-availability-guide/5002-wsfc-sap-sid-node-b.png

-[sap-ha-guide-figure-5003]:./media/virtual-machines-shared-sap-high-availability-guide/5003-sios-replicating-local-volume-b-to-a.png

-[sap-ha-guide-figure-6003]:./media/virtual-machines-shared-sap-high-availability-guide/6003-sap-multi-sid-full-landscape.png

-[sap-ha-guide-figure-8001]:./media/virtual-machines-shared-sap-high-availability-guide/8001.png

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-[sap-ha-guide-figure-8007]:./media/virtual-machines-shared-sap-high-availability-guide/8007.png

-[sap-ha-guide-figure-8008]:./media/virtual-machines-shared-sap-high-availability-guide/8008.png

-[sap-ha-guide-figure-8009]:./media/virtual-machines-shared-sap-high-availability-guide/8009.png

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-[sap-ha-guide-figure-8013]:./media/virtual-machines-shared-sap-high-availability-guide/8013.png

-[sap-ha-guide-figure-8014]:./media/virtual-machines-shared-sap-high-availability-guide/8014.png

-[sap-ha-guide-figure-8015]:./media/virtual-machines-shared-sap-high-availability-guide/8015.png

-[sap-ha-guide-figure-8016]:./media/virtual-machines-shared-sap-high-availability-guide/8016.png

-[sap-ha-guide-figure-8017]:./media/virtual-machines-shared-sap-high-availability-guide/8017.png

-[sap-ha-guide-figure-8018]:./media/virtual-machines-shared-sap-high-availability-guide/8018.png

-[sap-ha-guide-figure-8019]:./media/virtual-machines-shared-sap-high-availability-guide/8019.png

-[sap-ha-guide-figure-8020]:./media/virtual-machines-shared-sap-high-availability-guide/8020.png

-[sap-ha-guide-figure-8021]:./media/virtual-machines-shared-sap-high-availability-guide/8021.png

-[sap-ha-guide-figure-8022]:./media/virtual-machines-shared-sap-high-availability-guide/8022.png

-[sap-ha-guide-figure-8023]:./media/virtual-machines-shared-sap-high-availability-guide/8023.png

-[sap-ha-guide-figure-8024]:./media/virtual-machines-shared-sap-high-availability-guide/8024.png

-[sap-ha-guide-figure-8025]:./media/virtual-machines-shared-sap-high-availability-guide/8025.png

-[sap-templates-3-tier-multisid-xscs-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-xscs%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-xscs-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-xscs-md%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-db-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-db%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-db-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-db-md%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-apps-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-apps%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-apps-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-apps-md%2Fazuredeploy.json

-[virtual-machines-azure-resource-manager-architecture-benefits-arm]:../../azure-resource-manager/management/overview.md#the-benefits-of-using-resource-manager

-[virtual-machines-manage-availability]:../../virtual-machines-windows-manage-availability.md

-tags: azure-resource-manager

-keywords: ''

-[1928533]:https://launchpad.support.sap.com/#/notes/1928533

-[1999351]:https://launchpad.support.sap.com/#/notes/1999351

-[2015553]:https://launchpad.support.sap.com/#/notes/2015553

-[2178632]:https://launchpad.support.sap.com/#/notes/2178632

-[2243692]:https://launchpad.support.sap.com/#/notes/2243692

-[sap-installation-guides]:http://service.sap.com/instguides

-[tuning-failover-cluster-network-thresholds]:https://techcommunity.microsoft.com/t5/Failover-Clustering/Tuning-Failover-Cluster-Network-Thresholds/ba-p/371834

-[azure-resource-manager/management/azure-subscription-service-limits]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[azure-resource-manager/management/azure-subscription-service-limits-subscription]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[dbms-guide]:../../virtual-machines-windows-sap-dbms-guide-general.md

-[deployment-guide]:deployment-guide.md

-[dr-guide-classic]:https://go.microsoft.com/fwlink/?LinkID=521971

-[getting-started]:get-started.md

-[sap-high-availability-architecture-scenarios]:sap-high-availability-architecture-scenarios.md

-[sap-high-availability-guide-wsfc-file-share]:sap-high-availability-guide-wsfc-file-share.md

-[sap-ascs-high-availability-multi-sid-wsfc]:sap-ascs-high-availability-multi-sid-wsfc.md

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-hana-ha]:sap-hana-high-availability.md

-[sap-suse-ascs-ha]:high-availability-guide-suse.md

-[planning-guide]:planning-guide.md

-[planning-guide-11]:planning-guide.md

-[planning-guide-2.2]:planning-guide.md#f5b3b18c-302c-4bd8-9ab2-c388f1ab3d10

-[planning-guide-microsoft-azure-networking]:planning-guide.md#61678387-8868-435d-9f8c-450b2424f5bd

-[planning-guide-storage-microsoft-azure-storage-and-data-disks]:planning-guide.md#a72afa26-4bf4-4a25-8cf7-855d6032157f

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-high-availability-infrastructure-wsfc-shared-disk-vpn]:sap-high-availability-infrastructure-wsfc-shared-disk.md#c87a8d3f-b1dc-4d2f-b23c-da4b72977489

-[sap-high-availability-infrastructure-wsfc-shared-disk-change-def-ilb]:sap-high-availability-infrastructure-wsfc-shared-disk.md#fe0bd8b5-2b43-45e3-8295-80bee5415716

-[sap-high-availability-infrastructure-wsfc-shared-disk-setup-wsfc]:sap-high-availability-infrastructure-wsfc-shared-disk.md#0d67f090-7928-43e0-8772-5ccbf8f59aab

-[sap-high-availability-infrastructure-wsfc-shared-disk-collect-cluster-config]:sap-high-availability-infrastructure-wsfc-shared-disk.md#5eecb071-c703-4ccc-ba6d-fe9c6ded9d79

-[sap-high-availability-infrastructure-wsfc-shared-disk-install-sios]:sap-high-availability-infrastructure-wsfc-shared-disk.md#5c8e5482-841e-45e1-a89d-a05c0907c868

-[sap-high-availability-infrastructure-wsfc-shared-disk-add-dot-net]:sap-high-availability-infrastructure-wsfc-shared-disk.md#1c2788c3-3648-4e82-9e0d-e058e475e2a3

-[sap-high-availability-infrastructure-wsfc-shared-disk-install-sios-both-nodes]:sap-high-availability-infrastructure-wsfc-shared-disk.md#dd41d5a2-8083-415b-9878-839652812102

-[sap-high-availability-infrastructure-wsfc-shared-disk-setup-sios]:sap-high-availability-infrastructure-wsfc-shared-disk.md#d9c1fc8e-8710-4dff-bec2-1f535db7b006

-[Logo_Linux]:media/virtual-machines-shared-sap-shared/Linux.png

-[sap-ha-guide-figure-1000]:./media/virtual-machines-shared-sap-high-availability-guide/1000-wsfc-for-sap-ascs-on-azure.png

-[sap-ha-guide-figure-1001]:./media/virtual-machines-shared-sap-high-availability-guide/1001-wsfc-on-azure-ilb.png

-[sap-ha-guide-figure-1002]:./media/virtual-machines-shared-sap-high-availability-guide/1002-wsfc-sios-on-azure-ilb.png

-[sap-ha-guide-figure-2000]:./media/virtual-machines-shared-sap-high-availability-guide/2000-wsfc-sap-as-ha-on-azure.png

-[sap-ha-guide-figure-2001]:./media/virtual-machines-shared-sap-high-availability-guide/2001-wsfc-sap-ascs-ha-on-azure.png

-[sap-ha-guide-figure-2003]:./media/virtual-machines-shared-sap-high-availability-guide/2003-wsfc-sap-dbms-ha-on-azure.png

-[sap-ha-guide-figure-2004]:./media/virtual-machines-shared-sap-high-availability-guide/2004-wsfc-sap-ha-e2e-archit-template1-on-azure.png

-[sap-ha-guide-figure-2005]:./media/virtual-machines-shared-sap-high-availability-guide/2005-wsfc-sap-ha-e2e-arch-template2-on-azure.png

-[sap-ha-guide-figure-3000]:./media/virtual-machines-shared-sap-high-availability-guide/3000-template-parameters-sap-ha-arm-on-azure.png

-[sap-ha-guide-figure-3001]:./media/virtual-machines-shared-sap-high-availability-guide/3001-configuring-dns-servers-for-Azure-vnet.png

-[sap-ha-guide-figure-3002]:./media/virtual-machines-shared-sap-high-availability-guide/3002-configuring-static-IP-address-for-network-card-of-each-vm.png

-[sap-ha-guide-figure-3003]:./media/virtual-machines-shared-sap-high-availability-guide/3003-setup-static-ip-address-ilb-for-ascs-instance.png

-[sap-ha-guide-figure-3004]:./media/virtual-machines-shared-sap-high-availability-guide/3004-default-ascs-scs-ilb-balancing-rules-for-azure-ilb.png

-[sap-ha-guide-figure-3005]:./media/virtual-machines-shared-sap-high-availability-guide/3005-changing-ascs-scs-default-ilb-rules-for-azure-ilb.png

-[sap-ha-guide-figure-3006]:./media/virtual-machines-shared-sap-high-availability-guide/3006-adding-vm-to-domain.png

-[sap-ha-guide-figure-3007]:./media/virtual-machines-shared-sap-high-availability-guide/3007-config-wsfc-1.png

-[sap-ha-guide-figure-3008]:./media/virtual-machines-shared-sap-high-availability-guide/3008-config-wsfc-2.png

-[sap-ha-guide-figure-3009]:./media/virtual-machines-shared-sap-high-availability-guide/3009-config-wsfc-3.png

-[sap-ha-guide-figure-3010]:./media/virtual-machines-shared-sap-high-availability-guide/3010-config-wsfc-4.png

-[sap-ha-guide-figure-3011]:./media/virtual-machines-shared-sap-high-availability-guide/3011-config-wsfc-5.png

-[sap-ha-guide-figure-3012]:./media/virtual-machines-shared-sap-high-availability-guide/3012-config-wsfc-6.png

-[sap-ha-guide-figure-3013]:./media/virtual-machines-shared-sap-high-availability-guide/3013-config-wsfc-7.png

-[sap-ha-guide-figure-3014]:./media/virtual-machines-shared-sap-high-availability-guide/3014-config-wsfc-8.png

-[sap-ha-guide-figure-3015]:./media/virtual-machines-shared-sap-high-availability-guide/3015-config-wsfc-9.png

-[sap-ha-guide-figure-3016]:./media/virtual-machines-shared-sap-high-availability-guide/3016-config-wsfc-10.png

-[sap-ha-guide-figure-3017]:./media/virtual-machines-shared-sap-high-availability-guide/3017-config-wsfc-11.png

-[sap-ha-guide-figure-3018]:./media/virtual-machines-shared-sap-high-availability-guide/3018-config-wsfc-12.png

-[sap-ha-guide-figure-3019]:./media/virtual-machines-shared-sap-high-availability-guide/3019-assign-permissions-on-share-for-cluster-name-object.png

-[sap-ha-guide-figure-3020]:./media/virtual-machines-shared-sap-high-availability-guide/3020-change-object-type-include-computer-objects.png

-[sap-ha-guide-figure-3021]:./media/virtual-machines-shared-sap-high-availability-guide/3021-check-box-for-computer-objects.png

-[sap-ha-guide-figure-3022]:./media/virtual-machines-shared-sap-high-availability-guide/3022-set-security-attributes-for-cluster-name-object-on-file-share-quorum.png

-[sap-ha-guide-figure-3023]:./media/virtual-machines-shared-sap-high-availability-guide/3023-call-configure-cluster-quorum-setting-wizard.png

-[sap-ha-guide-figure-3024]:./media/virtual-machines-shared-sap-high-availability-guide/3024-selection-screen-different-quorum-configurations.png

-[sap-ha-guide-figure-3025]:./media/virtual-machines-shared-sap-high-availability-guide/3025-selection-screen-file-share-witness.png

-[sap-ha-guide-figure-3026]:./media/virtual-machines-shared-sap-high-availability-guide/3026-define-file-share-location-for-witness-share.png

-[sap-ha-guide-figure-3027]:./media/virtual-machines-shared-sap-high-availability-guide/3027-successful-reconfiguration-cluster-file-share-witness.png

-[sap-ha-guide-figure-3028]:./media/virtual-machines-shared-sap-high-availability-guide/3028-install-dot-net-framework-35.png

-[sap-ha-guide-figure-3029]:./media/virtual-machines-shared-sap-high-availability-guide/3029-install-dot-net-framework-35-progress.png

-[sap-ha-guide-figure-3046]:./media/virtual-machines-shared-sap-high-availability-guide/3046-dns-entry-sap-ascs-virtual-name-ip.png

-[sap-ha-guide-figure-3047]:./media/virtual-machines-shared-sap-high-availability-guide/3047-dns-manager.png

-[sap-ha-guide-figure-3048]:./media/virtual-machines-shared-sap-high-availability-guide/3048-default-cluster-probe-port.png

-[sap-ha-guide-figure-3049]:./media/virtual-machines-shared-sap-high-availability-guide/3049-cluster-probe-port-after.png

-[sap-ha-guide-figure-3050]:./media/virtual-machines-shared-sap-high-availability-guide/3050-service-type-ers-delayed-automatic.png

-[sap-ha-guide-figure-5000]:./media/virtual-machines-shared-sap-high-availability-guide/5000-wsfc-sap-sid-node-a.png

-[sap-ha-guide-figure-5001]:./media/virtual-machines-shared-sap-high-availability-guide/5001-sios-replicating-local-volume.png

-[sap-ha-guide-figure-5002]:./media/virtual-machines-shared-sap-high-availability-guide/5002-wsfc-sap-sid-node-b.png

-[sap-ha-guide-figure-5003]:./media/virtual-machines-shared-sap-high-availability-guide/5003-sios-replicating-local-volume-b-to-a.png

-[sap-ha-guide-figure-6003]:./media/virtual-machines-shared-sap-high-availability-guide/6003-sap-multi-sid-full-landscape.png

-[sap-templates-3-tier-multisid-xscs-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-xscs%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-xscs-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-xscs-md%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-db-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-db%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-db-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-db-md%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-apps-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-apps%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-apps-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-apps-md%2Fazuredeploy.json

-[virtual-machines-azure-resource-manager-architecture-benefits-arm]:../../azure-resource-manager/management/overview.md#the-benefits-of-using-resource-manager

-[virtual-machines-manage-availability]:../../virtual-machines-windows-manage-availability.md

-Before you begin the installation, review this article:

-* [Architecture guide: Cluster an SAP ASCS/SCS instance on a Windows failover cluster by using a cluster shared disk][sap-high-availability-guide-wsfc-shared-disk]

-**SAP deployment in Azure availability set**

-| 1st cluster node ASCS/SCS cluster | pr1-ascs-10 | 10.0.0.4 | pr1-ascs-avset | Premium_LRS |

-| 2nd cluster node ASCS/SCS cluster | pr1-ascs-11 | 10.0.0.5 | pr1-ascs-avset | |

-**SAP deployment in Azure availability zones**

-| 1st cluster node ASCS/SCS cluster | pr1-ascs-10 | 10.0.0.4 | AZ01 | Premium_ZRS |

-| 2nd cluster node ASCS/SCS cluster | pr1-ascs-11 | 10.0.0.5 | AZ02 | |

-> [!Note]

-> - If you are using PPG for SAP system deployed across zones like described in the document [Proximity placement groups with zonal deployments](proximity-placement-scenarios.md#proximity-placement-groups-with-zonal-deployments), you can attach Premium_ZRS storage to virtual machines sharing a disk.

-## <a name="fe0bd8b5-2b43-45e3-8295-80bee5415716"></a> Create Azure internal load balancer

-SAP ASCS, SAP SCS, and the new SAP ERS2, use virtual hostname and virtual IP addresses. On Azure a [load balancer](../../load-balancer/load-balancer-overview.md) is required to use a virtual IP address.

-We strongly recommend using [Standard load balancer](../../load-balancer/quickstart-load-balancer-standard-public-portal.md).

-> Floating IP is not supported on a NIC secondary IP configuration in load-balancing scenarios. For details see [Azure Load balancer Limitations](../../load-balancer/load-balancer-multivip-overview.md#limitations). If you need additional IP address for the VM, deploy a second NIC.

-The following list shows the configuration of the (A)SCS/ERS load balancer. The configuration for both SAP ASCS and ERS2 in performed in the same Azure load balancer.

-**(A)SCS**

- - Static ASCS/SCS IP address **10.0.0.43**

- Add all virtual machines that should be part of the (A)SCS/ERS cluster. In this example VMs **pr1-ascs-10** and **pr1-ascs-11**.

- - Port 620**nr**

- Leave the default option for Protocol (TCP), Interval (5), Unhealthy threshold (2)

- - If using Standard Load Balancer, select HA ports

- - If using Basic Load Balancer, create Load-balancing rules for the following ports

- - 32**nr** TCP

- - 36**nr** TCP

- - 39**nr** TCP

- - 81**nr** TCP

- - 5**nr**13 TCP

- - 5**nr**14 TCP

- - 5**nr**16 TCP

- - Make sure that Idle timeout (minutes) is set to max value 30, and that Floating IP (direct server return) is Enabled.

-**ERS2**

-As Enqueue Replication Server 2 (ERS2) is also clustered, ERS2 virtual IP address must be also configured on Azure ILB in addition to above SAP ASCS/SCS IP. This section only applies, if using Enqueue replication server 2 architecture.

- - Static SAP ERS2 IP address **10.0.0.44**

- The VMs were already added to the ILB backend pool.

- - Port 621**nr**

- Leave the default option for Protocol (TCP), Interval (5), Unhealthy threshold (2)

- - If using Standard Load Balancer, select HA ports

- - If using Basic Load Balancer, create Load-balancing rules for the following ports

- - 32**nr** TCP

- - 33**nr** TCP

- - 5**nr**13 TCP

- - 5**nr**14 TCP

- - 5**nr**16 TCP

- - Make sure that Idle timeout (minutes) is set to max value 30, and that Floating IP (direct server return) is Enabled.

-> The Azure ARM template will create two Windows 2019 or 2016 VMs, create Azure shared disk and attach to the VMs. Azure Internal Load Balancer will be created and configured as well.

-> For details - see the ARM template.

-## <a name="661035b2-4d0f-4d31-86f8-dc0a50d78158"></a> Add registry entries on both cluster nodes of the ASCS/SCS instance

-Azure Load Balancer may close connections, if the connections are idle for a period and exceed the idle timeout. The SAP work processes open connections to the SAP enqueue process as soon as the first enqueue/dequeue request needs to be sent. To avoid interrupting these connections, change the TCP/IP KeepAliveTime and KeepAliveInterval values on both cluster nodes. If using ERS1, it is also necessary to add SAP profile parameters, as described later in this article.

-## <a name="e69e9a34-4601-47a3-a41c-d2e11c626c0c"></a> Add the Windows VMs to the domain

-After you assign static IP addresses to the virtual machines, add the virtual machines to the domain.

-## <a name="0d67f090-7928-43e0-8772-5ccbf8f59aab"></a> Install and configure Windows failover cluster

-### Test and configure Windows failover cluster

-On Windows 2019, the cluster will automatically recognize that it is running in Azure, and as a default option for cluster management IP, it will use Distributed Network name. Therefore, it will use any of the cluster nodes local IP addresses. As a result, there is no need for a dedicated (virtual) network name for the cluster, and there is no need to configure this IP address on Azure Internal Load Balancer.

-

-These settings were tested with customers and offer a good compromise. They are resilient enough, but they also provide failover that is fast enough for real error conditions in SAP workloads or VM failure.

-This section is only applicable, if you are using Azure shared disk.

-Run this command on one of the cluster nodes. You will need to adjust the values for your resource group, Azure region, SAPSID, and so on.

-

- Get-Disk | Where-Object PartitionStyle -Eq "RAW" | Format-Table -AutoSize

- # Example output

- # Number Friendly Name Serial Number HealthStatus OperationalStatus Total Size Partition Style

- # - - -- -

- # 2 Msft Virtual Disk Healthy Online 512 GB RAW

-2. Format the disk. In this example, it is disk number 2.

- # Format SAP ASCS Disk number '2', with drive letter 'S'

- $SAPSID = "PR1"

- $DiskNumber = 2

- $DriveLetter = "S"

- $DiskLabel = "$SAPSID" + "SAP"

-

- Get-Disk -Number $DiskNumber | Where-Object PartitionStyle -Eq "RAW" | Initialize-Disk -PartitionStyle GPT -PassThru | New-Partition -DriveLetter $DriveLetter -UseMaximumSize | Format-Volume -FileSystem ReFS -NewFileSystemLabel $DiskLabel -Force -Verbose

- # Example outout

- # DriveLetter FileSystemLabel FileSystem DriveType HealthStatus OperationalStatus SizeRemaining Size

- # -- - -- - -

- # S PR1SAP ReFS Fixed Healthy OK 504.98 GB 511.81 GB

- # List all disks

- Get-ClusterAvailableDisk -All

- # Example output

- # Cluster : pr1clust

- # Id : 88ff1d94-0cf1-4c70-89ae-cbbb2826a484

- # Name : Cluster Disk 1

- # Number : 2

- # Size : 549755813888

- # Partitions : {\\?\GLOBALROOT\Device\Harddisk2\Partition2\}

- # Add the disk to cluster

- Get-ClusterAvailableDisk -All | Add-ClusterDisk

- # Example output

- # Name State OwnerGroup ResourceType

- # - -- -

- # Cluster Disk 1 Online Available Storage Physical Disk

-## <a name="5c8e5482-841e-45e1-a89d-a05c0907c868"></a> SIOS DataKeeper Cluster Edition for the SAP ASCS/SCS cluster share disk

-This section is only applicable, if you are using the third-party software SIOS DataKeeper Cluster Edition to create a mirrored storage that simulates cluster shared disk.

- _First page of the SIOS DataKeeper installation_

- _DataKeeper informs you that a service will be disabled_

- _User selection for SIOS DataKeeper_

- _Enter the domain user name and password for the SIOS DataKeeper installation_

- _Enter your SIOS DataKeeper license key_

- _SIOS DataKeeper Management and Configuration tool_

- _Insert the name or TCP/IP address of the first node the Management and Configuration tool should connect to, and in a second step, the second node_

- _Create a replication job_

- _Define the name of the replication job_

- _Define the base data for the node, which should be the current source node_

- _Define the name, TCP/IP address, and disk volume of the current target node_

- _Define replication details_

- _Select **Yes** to set the replicated volume as a cluster volume_

- _DataKeeper synchronous mirroring for the SAP ASCS/SCS share disk is active_

- _Failover Cluster Manager shows the disk that DataKeeper replicated_

-* [Install SAP NetWeaver HA by using a Windows failover cluster and shared disk for an SAP ASCS/SCS instance][sap-high-availability-installation-wsfc-shared-disk]

-tags: azure-resource-manager

-[1928533]:https://launchpad.support.sap.com/#/notes/1928533

-[1999351]:https://launchpad.support.sap.com/#/notes/1999351

-[2015553]:https://launchpad.support.sap.com/#/notes/2015553

-[2178632]:https://launchpad.support.sap.com/#/notes/2178632

-[2243692]:https://launchpad.support.sap.com/#/notes/2243692

-[1596496]:https://launchpad.support.sap.com/#/notes/1596496

-[sap-installation-guides]:http://service.sap.com/instguides

-[sap-powershell-scrips]:https://github.com/Azure-Samples/sap-powershell

-[azure-resource-manager/management/azure-subscription-service-limits]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[azure-resource-manager/management/azure-subscription-service-limits-subscription]:../../azure-resource-manager/management/azure-subscription-service-limits.md

-[dbms-guide]:../../virtual-machines-windows-sap-dbms-guide-general.md

-[deployment-guide]:deployment-guide.md

-[dr-guide-classic]:https://go.microsoft.com/fwlink/?LinkID=521971

-[getting-started]:get-started.md

-[sap-blog-new-sap-cluster-resource-dll]:https://blogs.sap.com/2017/08/28/new-sap-cluster-resource-dll-is-available/

-[sap-high-availability-architecture-scenarios]:sap-high-availability-architecture-scenarios.md

-[sap-high-availability-guide-wsfc-shared-disk]:sap-high-availability-guide-wsfc-shared-disk.md

-[sap-ascs-high-availability-multi-sid-wsfc]:sap-ascs-high-availability-multi-sid-wsfc.md

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-high-availability-installation-wsfc-shared-disk]:sap-high-availability-installation-wsfc-shared-disk.md

-[sap-hana-ha]:sap-hana-high-availability.md

-[sap-suse-ascs-ha]:high-availability-guide-suse.md

-[sap-high-availability-installation-wsfc-shared-disk]:sap-high-availability-installation-wsfc-shared-disk.md

-[planning-guide]:planning-guide.md

-[planning-guide-11]:planning-guide.md

-[planning-guide-2.1]:planning-guide.md#1625df66-4cc6-4d60-9202-de8a0b77f803

-[planning-guide-2.2]:planning-guide.md#f5b3b18c-302c-4bd8-9ab2-c388f1ab3d10

-[planning-guide-microsoft-azure-networking]:planning-guide.md#61678387-8868-435d-9f8c-450b2424f5bd

-[planning-guide-storage-microsoft-azure-storage-and-data-disks]:planning-guide.md#a72afa26-4bf4-4a25-8cf7-855d6032157f

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-high-availability-infrastructure-wsfc-shared-disk]:sap-high-availability-infrastructure-wsfc-shared-disk.md

-[sap-high-availability-infrastructure-wsfc-shared-disk-vpn]:sap-high-availability-infrastructure-wsfc-shared-disk.md#c87a8d3f-b1dc-4d2f-b23c-da4b72977489

-[sap-high-availability-infrastructure-wsfc-shared-disk-change-def-ilb]:sap-high-availability-infrastructure-wsfc-shared-disk.md#fe0bd8b5-2b43-45e3-8295-80bee5415716

-[sap-high-availability-infrastructure-wsfc-shared-disk-setup-wsfc]:sap-high-availability-infrastructure-wsfc-shared-disk.md#0d67f090-7928-43e0-8772-5ccbf8f59aab

-[sap-high-availability-infrastructure-wsfc-shared-disk-collect-cluster-config]:sap-high-availability-infrastructure-wsfc-shared-disk.md#5eecb071-c703-4ccc-ba6d-fe9c6ded9d79

-[sap-high-availability-infrastructure-wsfc-shared-disk-install-sios]:sap-high-availability-infrastructure-wsfc-shared-disk.md#5c8e5482-841e-45e1-a89d-a05c0907c868

-[sap-high-availability-infrastructure-wsfc-shared-disk-add-dot-net]:sap-high-availability-infrastructure-wsfc-shared-disk.md#1c2788c3-3648-4e82-9e0d-e058e475e2a3

-[sap-high-availability-infrastructure-wsfc-shared-disk-install-sios-both-nodes]:sap-high-availability-infrastructure-wsfc-shared-disk.md#dd41d5a2-8083-415b-9878-839652812102

-[sap-high-availability-infrastructure-wsfc-shared-disk-setup-sios]:sap-high-availability-infrastructure-wsfc-shared-disk.md#d9c1fc8e-8710-4dff-bec2-1f535db7b006

-[sap-ha-guide-figure-1000]:./media/virtual-machines-shared-sap-high-availability-guide/1000-wsfc-for-sap-ascs-on-azure.png

-[sap-ha-guide-figure-1001]:./media/virtual-machines-shared-sap-high-availability-guide/1001-wsfc-on-azure-ilb.png

-[sap-ha-guide-figure-1002]:./media/virtual-machines-shared-sap-high-availability-guide/1002-wsfc-sios-on-azure-ilb.png

-[sap-ha-guide-figure-2000]:./media/virtual-machines-shared-sap-high-availability-guide/2000-wsfc-sap-as-ha-on-azure.png

-[sap-ha-guide-figure-2001]:./media/virtual-machines-shared-sap-high-availability-guide/2001-wsfc-sap-ascs-ha-on-azure.png

-[sap-ha-guide-figure-2003]:./media/virtual-machines-shared-sap-high-availability-guide/2003-wsfc-sap-dbms-ha-on-azure.png

-[sap-ha-guide-figure-2004]:./media/virtual-machines-shared-sap-high-availability-guide/2004-wsfc-sap-ha-e2e-archit-template1-on-azure.png

-[sap-ha-guide-figure-2005]:./media/virtual-machines-shared-sap-high-availability-guide/2005-wsfc-sap-ha-e2e-arch-template2-on-azure.png

-[sap-ha-guide-figure-3000]:./media/virtual-machines-shared-sap-high-availability-guide/3000-template-parameters-sap-ha-arm-on-azure.png

-[sap-ha-guide-figure-3001]:./media/virtual-machines-shared-sap-high-availability-guide/3001-configuring-dns-servers-for-Azure-vnet.png

-[sap-ha-guide-figure-3002]:./media/virtual-machines-shared-sap-high-availability-guide/3002-configuring-static-IP-address-for-network-card-of-each-vm.png

-[sap-ha-guide-figure-3003]:./media/virtual-machines-shared-sap-high-availability-guide/3003-setup-static-ip-address-ilb-for-ascs-instance.png

-[sap-ha-guide-figure-3004]:./media/virtual-machines-shared-sap-high-availability-guide/3004-default-ascs-scs-ilb-balancing-rules-for-azure-ilb.png

-[sap-ha-guide-figure-3005]:./media/virtual-machines-shared-sap-high-availability-guide/3005-changing-ascs-scs-default-ilb-rules-for-azure-ilb.png

-[sap-ha-guide-figure-3006]:./media/virtual-machines-shared-sap-high-availability-guide/3006-adding-vm-to-domain.png

-[sap-ha-guide-figure-3007]:./media/virtual-machines-shared-sap-high-availability-guide/3007-config-wsfc-1.png

-[sap-ha-guide-figure-3008]:./media/virtual-machines-shared-sap-high-availability-guide/3008-config-wsfc-2.png

-[sap-ha-guide-figure-3009]:./media/virtual-machines-shared-sap-high-availability-guide/3009-config-wsfc-3.png

-[sap-ha-guide-figure-3010]:./media/virtual-machines-shared-sap-high-availability-guide/3010-config-wsfc-4.png

-[sap-ha-guide-figure-3011]:./media/virtual-machines-shared-sap-high-availability-guide/3011-config-wsfc-5.png

-[sap-ha-guide-figure-3012]:./media/virtual-machines-shared-sap-high-availability-guide/3012-config-wsfc-6.png

-[sap-ha-guide-figure-3013]:./media/virtual-machines-shared-sap-high-availability-guide/3013-config-wsfc-7.png

-[sap-ha-guide-figure-3014]:./media/virtual-machines-shared-sap-high-availability-guide/3014-config-wsfc-8.png

-[sap-ha-guide-figure-3015]:./media/virtual-machines-shared-sap-high-availability-guide/3015-config-wsfc-9.png

-[sap-ha-guide-figure-3016]:./media/virtual-machines-shared-sap-high-availability-guide/3016-config-wsfc-10.png

-[sap-ha-guide-figure-3017]:./media/virtual-machines-shared-sap-high-availability-guide/3017-config-wsfc-11.png

-[sap-ha-guide-figure-3018]:./media/virtual-machines-shared-sap-high-availability-guide/3018-config-wsfc-12.png

-[sap-ha-guide-figure-3019]:./media/virtual-machines-shared-sap-high-availability-guide/3019-assign-permissions-on-share-for-cluster-name-object.png

-[sap-ha-guide-figure-3020]:./media/virtual-machines-shared-sap-high-availability-guide/3020-change-object-type-include-computer-objects.png

-[sap-ha-guide-figure-3021]:./media/virtual-machines-shared-sap-high-availability-guide/3021-check-box-for-computer-objects.png

-[sap-ha-guide-figure-3022]:./media/virtual-machines-shared-sap-high-availability-guide/3022-set-security-attributes-for-cluster-name-object-on-file-share-quorum.png

-[sap-ha-guide-figure-3023]:./media/virtual-machines-shared-sap-high-availability-guide/3023-call-configure-cluster-quorum-setting-wizard.png

-[sap-ha-guide-figure-3024]:./media/virtual-machines-shared-sap-high-availability-guide/3024-selection-screen-different-quorum-configurations.png

-[sap-ha-guide-figure-3025]:./media/virtual-machines-shared-sap-high-availability-guide/3025-selection-screen-file-share-witness.png

-[sap-ha-guide-figure-3026]:./media/virtual-machines-shared-sap-high-availability-guide/3026-define-file-share-location-for-witness-share.png

-[sap-ha-guide-figure-3027]:./media/virtual-machines-shared-sap-high-availability-guide/3027-successful-reconfiguration-cluster-file-share-witness.png

-[sap-ha-guide-figure-3028]:./media/virtual-machines-shared-sap-high-availability-guide/3028-install-dot-net-framework-35.png

-[sap-ha-guide-figure-3029]:./media/virtual-machines-shared-sap-high-availability-guide/3029-install-dot-net-framework-35-progress.png

-[sap-ha-guide-figure-3030]:./media/virtual-machines-shared-sap-high-availability-guide/3030-sios-installer.png

-[sap-ha-guide-figure-3031]:./media/virtual-machines-shared-sap-high-availability-guide/3031-first-screen-sios-data-keeper-installation.png

-[sap-ha-guide-figure-3032]:./media/virtual-machines-shared-sap-high-availability-guide/3032-data-keeper-informs-service-be-disabled.png

-[sap-ha-guide-figure-3033]:./media/virtual-machines-shared-sap-high-availability-guide/3033-user-selection-sios-data-keeper.png

-[sap-ha-guide-figure-3034]:./media/virtual-machines-shared-sap-high-availability-guide/3034-domain-user-sios-data-keeper.png

-[sap-ha-guide-figure-3035]:./media/virtual-machines-shared-sap-high-availability-guide/3035-provide-sios-data-keeper-license.png

-[sap-ha-guide-figure-3036]:./media/virtual-machines-shared-sap-high-availability-guide/3036-data-keeper-management-config-tool.png

-[sap-ha-guide-figure-3037]:./media/virtual-machines-shared-sap-high-availability-guide/3037-tcp-ip-address-first-node-data-keeper.png

-[sap-ha-guide-figure-3038]:./media/virtual-machines-shared-sap-high-availability-guide/3038-create-replication-sios-job.png

-[sap-ha-guide-figure-3039]:./media/virtual-machines-shared-sap-high-availability-guide/3039-define-sios-replication-job-name.png

-[sap-ha-guide-figure-3040]:./media/virtual-machines-shared-sap-high-availability-guide/3040-define-sios-source-node.png

-[sap-ha-guide-figure-3041]:./media/virtual-machines-shared-sap-high-availability-guide/3041-define-sios-target-node.png

-[sap-ha-guide-figure-3042]:./media/virtual-machines-shared-sap-high-availability-guide/3042-define-sios-synchronous-replication.png

-[sap-ha-guide-figure-3043]:./media/virtual-machines-shared-sap-high-availability-guide/3043-enable-sios-replicated-volume-as-cluster-volume.png

-[sap-ha-guide-figure-3044]:./media/virtual-machines-shared-sap-high-availability-guide/3044-data-keeper-synchronous-mirroring-for-SAP-gui.png

-[sap-ha-guide-figure-3045]:./media/virtual-machines-shared-sap-high-availability-guide/3045-replicated-disk-by-data-keeper-in-wsfc.png

-[sap-ha-guide-figure-3046]:./media/virtual-machines-shared-sap-high-availability-guide/3046-dns-entry-sap-ascs-virtual-name-ip.png

-[sap-ha-guide-figure-3047]:./media/virtual-machines-shared-sap-high-availability-guide/3047-dns-manager.png

-[sap-ha-guide-figure-3048]:./media/virtual-machines-shared-sap-high-availability-guide/3048-default-cluster-probe-port.png

-[sap-ha-guide-figure-3049]:./media/virtual-machines-shared-sap-high-availability-guide/3049-cluster-probe-port-after.png

-[sap-ha-guide-figure-3050]:./media/virtual-machines-shared-sap-high-availability-guide/3050-service-type-ers-delayed-automatic.png

-[sap-ha-guide-figure-5000]:./media/virtual-machines-shared-sap-high-availability-guide/5000-wsfc-sap-sid-node-a.png

-[sap-ha-guide-figure-5001]:./media/virtual-machines-shared-sap-high-availability-guide/5001-sios-replicating-local-volume.png

-[sap-ha-guide-figure-5002]:./media/virtual-machines-shared-sap-high-availability-guide/5002-wsfc-sap-sid-node-b.png

-[sap-ha-guide-figure-5003]:./media/virtual-machines-shared-sap-high-availability-guide/5003-sios-replicating-local-volume-b-to-a.png

-[sap-ha-guide-figure-6003]:./media/virtual-machines-shared-sap-high-availability-guide/6003-sap-multi-sid-full-landscape.png

-[sap-ha-guide-figure-8001]:./media/virtual-machines-shared-sap-high-availability-guide/8001.png

-[sap-ha-guide-figure-8002]:./media/virtual-machines-shared-sap-high-availability-guide/8002.png

-[sap-ha-guide-figure-8003]:./media/virtual-machines-shared-sap-high-availability-guide/8003.png

-[sap-ha-guide-figure-8004]:./media/virtual-machines-shared-sap-high-availability-guide/8004.png

-[sap-ha-guide-figure-8005]:./media/virtual-machines-shared-sap-high-availability-guide/8005.png

-[sap-ha-guide-figure-8006]:./media/virtual-machines-shared-sap-high-availability-guide/8006.png

-[sap-ha-guide-figure-8007]:./media/virtual-machines-shared-sap-high-availability-guide/8007.png

-[sap-ha-guide-figure-8008]:./media/virtual-machines-shared-sap-high-availability-guide/8008.png

-[sap-ha-guide-figure-8009]:./media/virtual-machines-shared-sap-high-availability-guide/8009.png

-[sap-ha-guide-figure-8010]:./media/virtual-machines-shared-sap-high-availability-guide/8010.png

-[sap-ha-guide-figure-8011]:./media/virtual-machines-shared-sap-high-availability-guide/8011.png

-[sap-ha-guide-figure-8012]:./media/virtual-machines-shared-sap-high-availability-guide/8012.png

-[sap-ha-guide-figure-8013]:./media/virtual-machines-shared-sap-high-availability-guide/8013.png

-[sap-ha-guide-figure-8014]:./media/virtual-machines-shared-sap-high-availability-guide/8014.png

-[sap-ha-guide-figure-8015]:./media/virtual-machines-shared-sap-high-availability-guide/8015.png

-[sap-ha-guide-figure-8016]:./media/virtual-machines-shared-sap-high-availability-guide/8016.png

-[sap-ha-guide-figure-8017]:./media/virtual-machines-shared-sap-high-availability-guide/8017.png

-[sap-ha-guide-figure-8018]:./media/virtual-machines-shared-sap-high-availability-guide/8018.png

-[sap-ha-guide-figure-8019]:./media/virtual-machines-shared-sap-high-availability-guide/8019.png

-[sap-ha-guide-figure-8020]:./media/virtual-machines-shared-sap-high-availability-guide/8020.png

-[sap-ha-guide-figure-8021]:./media/virtual-machines-shared-sap-high-availability-guide/8021.png

-[sap-ha-guide-figure-8022]:./media/virtual-machines-shared-sap-high-availability-guide/8022.png

-[sap-ha-guide-figure-8023]:./media/virtual-machines-shared-sap-high-availability-guide/8023.png

-[sap-ha-guide-figure-8024]:./media/virtual-machines-shared-sap-high-availability-guide/8024.png

-[sap-ha-guide-figure-8025]:./media/virtual-machines-shared-sap-high-availability-guide/8025.png

-[sap-templates-3-tier-multisid-xscs-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-xscs%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-xscs-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-xscs-md%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-db-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-db%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-db-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-db-md%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-apps-marketplace-image]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fsap-3-tier-marketplace-image-multi-sid-apps%2Fazuredeploy.json

-[sap-templates-3-tier-multisid-apps-marketplace-image-md]:https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2Fapplication-workloads%2Fsap%2Fsap-3-tier-marketplace-image-multi-sid-apps-md%2Fazuredeploy.json

-[virtual-machines-azure-resource-manager-architecture-benefits-arm]:../../azure-resource-manager/management/overview.md#the-benefits-of-using-resource-manager

-[virtual-machines-manage-availability]:../../virtual-machines-windows-manage-availability.md

-* [High availability for SAP NetWeaver on Azure VMs]

-*

-> Clustering SAP ASCS/SCS instances by using a file share is supported for SAP NetWeaver 7.40 (and later), with SAP Kernel 7.49 (and later).

->

-> [!IMPORTANT]

-> The setup must meet the following requirement: the SAP ASCS/SCS instances and the SOFS share must be deployed in separate clusters.

->

->

- * The \<DOMAIN>\SAP_\<SID>_GlobalAdmin user group

- * The SAP ASCS/SCS cluster node computer objects \<DOMAIN>\ClusterNode1$ and \<DOMAIN>\ClusterNode2$

- ```

-Restart the SAP ASCS/SCS instance.

-Set `KeepAlive` parameters on both SAP ASCS/SCS cluster nodes follow the instructions to [Set registry entries on the cluster nodes of the SAP ASCS/SCS instance](./sap-high-availability-infrastructure-wsfc-shared-disk.md#661035b2-4d0f-4d31-86f8-dc0a50d78158).

-* [Install an ASCS/SCS instance on a failover cluster with no shared disks - Official SAP guidelines for high-availability file share][sap-official-ha-file-share-document]

-* [Storage Spaces Direct in Windows Server 2016][s2d-in-win-2016]

-* [Scale-Out File Server for application data overview][sofs-overview]

-* [What's new in storage in Windows Server 2016][new-in-win-2016-storage]

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-keywords: ''

-Azure AI Search's performance depends on a [variety of factors](search-performance-tips.md) including the size of your search service and the types of queries you're sending. To help estimate the size of search service needed for your workload, we've run several benchmarks to document the performance for different search services and configurations. These benchmarks in no way guarantee a certain level of performance from your service but can give you an idea of the performance you can expect.

-### Disclaimer

-These benchmarks in no way guarantee a certain level of performance from your service but can give you an idea of the performance you can expect based on your scenario.

-Query latency varies based on the load of the service and services under higher stress will have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.

-Query latency varies based on the load of the service and services under higher stress will have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.

-Query latency varies based on the load of the service and services under higher stress will have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.

-Query latency varies based on the load of the service and services under higher stress will have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.

-Query latency varies based on the load of the service and services under higher stress will have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.

-Query latency varies based on the load of the service and services under higher stress will have a higher average query latency. The following table shows the 25th, 50th, 75th, 90th, 95th, and 99th percentiles of query latency for three different usage levels.

-* An S2 will typically have lower latency than an S1 at comparable query volumes

-* The higher the load on the service, the higher the average latency will be

-Search indexes can have vector and non-vector fields. You can create pure vector queries, or hybrid queries targeting both vector *and* textual fields configured for filters, sorts, facets, and semantic reranking.

-> Looking for [built-in data chunking and vectorization public preview](vector-search-integrated-vectorization.md)? Try the [**Import and vectorize data** wizard](search-get-started-portal-import-vectors.md) instead.

- For the optional [semantic ranking](semantic-search-overview.md) shown in the last example, your search service must be Basic tier or higher, with [semantic ranking enabled](semantic-how-to-enable-disable.md).

-+ [Sample Postman collection](https://github.com/Azure-Samples/azure-search-postman-samples/tree/main/Quickstart-vectors), with requests targeting the **2023-11-01** API version of Azure AI Search.

-+ The `"fields"` collection includes a required key field, text and vector fields (such as `"Description"`, `"DescriptionVector"`) for keyword and vector search. Colocating vector and non-vector fields in the same index enables hybrid queries. For instance, you can combine filters, keyword search with semantic ranking, and vectors into a single query operation.

-You can add filters, but the filters are applied to the non-vector content in your index. In this example, the filter applies to the `"Tags"` field, filtering out any hotels that don't provide free WIFI.

-> The Microsoft Sentinel for SAP solution is free to install, but there will be an [additional hourly charge](https://azure.microsoft.com/pricing/offers/microsoft-sentinel-sap-promo/) for activating and using the solution on production systems starting May 2023.

-> - The additional hourly charge applies to connected production systems only.

-> - Microsoft Sentinel identifies a production system by looking at the configuration on the SAP system. To do this, Microsoft Sentinel searches for a production entry in the T000 table.

-> - [View the roles of your connected production systems](../monitor-sap-system-health.md).

-The Microsoft Sentinel for SAP data connector is an agent, installed on a VM, a physical server, or a Kubernetes cluster that collects application logs from across the entire SAP system landscape for all of your SAP SIDs. It then sends those logs to your Log Analytics workspace in Microsoft Sentinel. You can then use the other content in the Threat Monitoring for SAP solution ΓÇô the analytics rules, workbooks, and watchlists ΓÇô to gain insight into your organization's SAP environment and to detect and respond to security threats.

- This diagram shows a multi-SID SAP landscape with a split between productive and non-productive systems including the SAP Business Technology Platform. All of the systems and services are being onboarded to the Sentinel for SAP solution.

-The Sentinel for SAP solution uses a concept of central user tagging, designed to allow for lower false positive rate with minimal effort on the customer end:

-Here is a KQL query demonstrating the use case described below:

-let excludeUsersTags= dynamic(['RunObsoleteProgOK']);

-let excludedUsers= SAPUsersGetVIP(SearchForTags= dynamic(["RunObsoleteProgOK"]))| summarize by User2Exclude=SAPUser;

-SAPAuditLog

-This functionality is heavily used in the Deterministic and Anomalous Audit Log Monitor Alerts, 'where tags can be associated with SAP audit log message ID, and can also be easily extended to custom alert rules.

- - Optional

- - Default value: dynamic('All Tags')

- - When SearchForTags equals 'All Tags', all users are returned along with their tags, else, only users bearing the tags specified in SearchForTags are returned. TagsIntersect will show which tags were found, and IntersectionSize will hold the count of those.

- - Optional

- - Default value: "Do not return any in-focus users"

- - The function returns all users bearing the tags specified in SpecialFocusTags, and marked those with specialFocusTagged = true.

-| The "SAP User Config" watchlist | SearchKey | Search Key |

-| The "SAP User Config" watchlist | SAPUser | The SAP User | OSS, DDIC

-| The "SAP User Config" watchlist | Tags | string of tags assigned to user | RunObsoleteProgOK

-| The "SAP User Config" watchlist | User's Microsoft Entra Object ID | Microsoft Entra Object ID |

-| The "SAP User Config" watchlist | User Identifier | AD User Identifier |

-| The "SAP User Config" watchlist | User on-premises Sid | |

-| The "SAP User Config" watchlist | User Principal Name | |

-| The "SAP User Config" watchlist | TagsList | A list of tags assigned to user | ChangeUserMasterDataOK;RunObsoleteProgOK

-| Logic | TagsIntersect | A set of tags that matched SearchForTags | ["ChangeUserMasterDataOK","RunObsoleteProgOK"]

-## What Microsoft Sentinel solution for SAP® applications does

-## Solution details

- This diagram shows a multi-SID SAP landscape with a split between productive and non-productive systems including the SAP Business Technology Platform. All of the systems and services are being onboarded to the Sentinel for SAP solution.

-tags:

-tags:

-This article explains how to connect to an Elastic storage area network (SAN) volume from a Linux client. For details on connecting from a Windows client, see [Connect to Elastic SAN Preview volumes - Windows](elastic-san-connect-windows.md).

-This article explains how to connect to an Elastic storage area network (SAN) volume from a Windows client. For details on connecting from a Linux client, see [Connect to Elastic SAN Preview volumes - Linux](elastic-san-connect-linux.md).

-#