Merge branch 'main' into WI227197-support-mysql

ElazarK · web-flow · commit 795d19df0383 · 2024-04-02T15:09:08.000+03:00
diff --git a/articles/ai-studio/tutorials/deploy-copilot-ai-studio.md b/articles/ai-studio/tutorials/deploy-copilot-ai-studio.md
@@ -414,6 +414,7 @@ Now that you have your evaluation dataset, you can evaluate your flow by followi
 
     > [!NOTE]
     > Evaluation with AI-assisted metrics needs to call another GPT model to do the calculation. For best performance, use a GPT-4 or gpt-35-turbo-16k model. If you didn't previously deploy a GPT-4 or gpt-35-turbo-16k model, you can deploy another model by following the steps in [Deploy a chat model](#deploy-a-chat-model). Then return to this step and select the model you deployed.
+    > The evaluation process may take up lots of tokens, so it's recommended to use a model which can support >=16k tokens.
 
 1. Select **Add new dataset**. Then select **Next**.
 
diff --git a/articles/azure-monitor/alerts/action-groups.md b/articles/azure-monitor/alerts/action-groups.md
@@ -3,7 +3,7 @@ title: Azure Monitor action groups
 description: Find out how to create and manage action groups. Learn about notifications and actions that action groups enable, such as email, webhooks, and Azure functions.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 05/02/2023
+ms.date: 04/01/2024
 ms.reviewer: jagummersall
 ms.custom: references_regions, devx-track-arm-template, has-azure-ad-ps-ref, azure-ad-ref-level-one-done
 ---
diff --git a/articles/azure-monitor/alerts/activity-log-alerts-webhook.md b/articles/azure-monitor/alerts/activity-log-alerts-webhook.md
@@ -2,9 +2,9 @@
 title: Configure a webhook to get activity log alerts
 description: Learn about the schema of the JSON that's posted to a webhook URL when an activity log alert activates.
 ms.author: abbyweisberg
-ms.reviewer: nolavime
+ms.reviewer: yagil
 ms.topic: conceptual
-ms.date: 03/31/2017
+ms.date: 04/01/2024
 ---
 
 # Configure a webhook to get activity log alerts
diff --git a/articles/azure-monitor/alerts/alerts-smart-detections-migration.md b/articles/azure-monitor/alerts/alerts-smart-detections-migration.md
@@ -4,7 +4,7 @@ description: Learn about the steps required to upgrade your Azure Monitor Applic
 ms.author: abbyweisberg
 ms.reviewer: yagil
 ms.topic: conceptual
-ms.date: 2/23/2022
+ms.date: 04/01/2024
 ---
 
 # Migrate Azure Monitor Application Insights smart detection to alerts (preview)
diff --git a/articles/azure-monitor/alerts/proactive-application-security-detection-pack.md b/articles/azure-monitor/alerts/proactive-application-security-detection-pack.md
@@ -3,7 +3,7 @@ title: Security detection Pack with Azure Application Insights
 description: Monitor application with Azure Application Insights and smart detection for potential security issues.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 12/12/2017
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/proactive-arm-config.md b/articles/azure-monitor/alerts/proactive-arm-config.md
@@ -4,7 +4,7 @@ description: Automate management and configuration of Application Insights smart
 ms.author: abbyweisberg
 ms.topic: conceptual
 ms.custom: devx-track-arm-template
-ms.date: 02/14/2021
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/proactive-diagnostics.md b/articles/azure-monitor/alerts/proactive-diagnostics.md
@@ -3,7 +3,7 @@ title: Smart detection in Application Insights | Microsoft Docs
 description: Application Insights performs automatic deep analysis of your app telemetry and warns you about potential problems.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 02/07/2019
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/proactive-email-notification.md b/articles/azure-monitor/alerts/proactive-email-notification.md
@@ -3,7 +3,7 @@ title: Smart Detection notification change - Azure Application Insights
 description: Change to the default notification recipients from Smart Detection. Smart Detection lets you monitor application traces with Azure Application Insights for unusual patterns in trace telemetry.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 02/14/2021
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/proactive-exception-volume.md b/articles/azure-monitor/alerts/proactive-exception-volume.md
@@ -3,7 +3,7 @@ title: Abnormal rise in exception volume - Azure Application Insights
 description: Monitor application exceptions with smart detection in Azure Application Insights for unusual patterns in exception volume.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 12/08/2017
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/proactive-failure-diagnostics.md b/articles/azure-monitor/alerts/proactive-failure-diagnostics.md
@@ -3,8 +3,8 @@ title: Smart Detection of Failure Anomalies in Application Insights | Microsoft
 description: Alerts you to unusual changes in the rate of failed requests to your web app, and provides diagnostic analysis. No configuration is needed.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 12/18/2018
-ms.reviewer: yalavi
+ms.date: 04/01/2024
+ms.reviewer: yagil
 ---
 
 # Smart Detection - Failure Anomalies
diff --git a/articles/azure-monitor/alerts/proactive-potential-memory-leak.md b/articles/azure-monitor/alerts/proactive-potential-memory-leak.md
@@ -3,7 +3,7 @@ title: 'Detect memory leak: Application Insights smart detection'
 description: Monitor applications with Application Insights for potential memory leaks.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 12/12/2017
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/proactive-trace-severity.md b/articles/azure-monitor/alerts/proactive-trace-severity.md
@@ -3,7 +3,7 @@ title: Degradation in trace severity ratio - Azure Application Insights
 description: Monitor application traces with Azure Application Insights for unusual patterns in trace telemetry with smart detection.
 ms.author: abbyweisberg
 ms.topic: conceptual
-ms.date: 11/27/2017
+ms.date: 04/01/2024
 ms.reviewer: yagil
 ---
 
diff --git a/articles/azure-monitor/alerts/prometheus-alerts.md b/articles/azure-monitor/alerts/prometheus-alerts.md
@@ -5,7 +5,7 @@ author: bwren
 ms.author: abbyweisberg
 ms.reviewer: yagil
 ms.topic: conceptual
-ms.date: 09/15/2022
+ms.date: 04/01/2024
 ---
 
 # Prometheus alerts in Azure Monitor
diff --git a/articles/azure-monitor/alerts/resource-manager-alerts-activity-log.md b/articles/azure-monitor/alerts/resource-manager-alerts-activity-log.md
@@ -6,7 +6,7 @@ ms.custom: devx-track-arm-template
 author: AbbyMSFT
 ms.author: abbyweisberg
 ms.reviewer: yagil
-ms.date: 12/28/2022
+ms.date: 04/01/2024
 ---
 
 # Resource Manager template samples for Azure Monitor activity log alert rules (Administrative category) 
diff --git a/articles/azure-monitor/alerts/resource-manager-alerts-resource-health.md b/articles/azure-monitor/alerts/resource-manager-alerts-resource-health.md
@@ -6,7 +6,7 @@ ms.custom: devx-track-arm-template
 author: AbbyMSFT
 ms.author: abbyweisberg
 ms.reviewer: yagil
-ms.date: 05/11/2022
+ms.date: 04/01/2024
 ---
 
 # Resource Manager template samples for resource health alert rules in Azure Monitor
diff --git a/articles/azure-monitor/alerts/smart-detection-performance.md b/articles/azure-monitor/alerts/smart-detection-performance.md
@@ -4,7 +4,7 @@ description: Smart detection analyzes your app telemetry and warns you of potent
 ms.author: abbyweisberg
 ms.reviewer: yagil
 ms.topic: conceptual
-ms.date: 05/04/2017
+ms.date: 04/01/2024
 ---
 
 # Smart detection - Performance Anomalies
diff --git a/articles/defender-for-cloud/upcoming-changes.md b/articles/defender-for-cloud/upcoming-changes.md
@@ -25,7 +25,9 @@ If you're looking for the latest release notes, you can find them in the [What's
 
 | Planned change | Announcement date | Estimated date for change |
 |--|--|--|
-| [General availability of Unified Disk Encryption recommendations](#general-availability-of-unified-disk-encryption-recommendations) | March 28, 2024 | April 30, 2024 |
+| [Deprecating of virtual machine recommendation](#deprecating-of-virtual-machine-recommendation) | April 2, 2024 | April 30, 2024 |
+| [General Availability of Unified Disk Encryption recommendations](#general-availability-of-unified-disk-encryption-recommendations) | March 28, 2024 | April 30, 2024 |
+| [Defender for open-source relational databases updates](#defender-for-open-source-relational-databases-updates) | March 6, 2024 | April, 2024 |
 | [Changes in where you access Compliance offerings and Microsoft Actions](#changes-in-where-you-access-compliance-offerings-and-microsoft-actions) | March 3, 2024 | September 30, 2025 |
 | [Microsoft Security Code Analysis (MSCA) is no longer operational](#microsoft-security-code-analysis-msca-is-no-longer-operational) | February 26, 2024 | February 26, 2024 |
 | [Decommissioning of Microsoft.SecurityDevOps resource provider](#decommissioning-of-microsoftsecuritydevops-resource-provider) | February 5, 2024 | March 6, 2024 |
@@ -43,7 +45,16 @@ If you're looking for the latest release notes, you can find them in the [What's
 | [Deprecating two security incidents](#deprecating-two-security-incidents) |  | November 2023 |
 | [Defender for Cloud plan and strategy for the Log Analytics agent deprecation](#defender-for-cloud-plan-and-strategy-for-the-log-analytics-agent-deprecation) |  | August 2024 |
 
-## General availability of Unified Disk Encryption recommendations
+
+## Deprecating of virtual machine recommendation
+
+**Announcement date: April 2, 2024**
+
+**Estimated date of change: April 30, 2024**
+
+The recommendation [Virtual machines should be migrated to new Azure Resource Manager resources](https://portal.azure.com/#blade/Microsoft_Azure_Security/RecommendationsBlade/assessmentKey/12018f4f-3d10-999b-e4c4-86ec25be08a1) is set to be deprecated. There should be no effect on customers as these resources no longer exist.
+
+## General Availability of Unified Disk Encryption recommendations
 
 **Announcement date: March 28, 2024**
 
diff --git a/articles/machine-learning/prompt-flow/how-to-bulk-test-evaluate-flow.md b/articles/machine-learning/prompt-flow/how-to-bulk-test-evaluate-flow.md
@@ -81,6 +81,7 @@ If an evaluation method uses Large Language Models (LLMs) to measure the perform
 
 > [!NOTE]
 > Some evaluation methods require GPT-4 or GPT-3 to run. You must provide valid connections for these evaluation methods before using them.
+> Some evaluation process may take up lots of tokens, so it's recommended to use a model which can support >=16k tokens.
 
 After you finish the input mapping, select on  **"Next"**  to review your settings and select on  **"Submit"**  to start the batch run with evaluation.
 
@@ -125,7 +126,7 @@ You can select **Evaluate** to start another round of evaluation.
 
 After setting up the configuration, you can select **"Submit"** for this new round of evaluation. After submission, you'll be able to see a new record in the prompt flow run list.
 
-After the evaluation run completed, similarly, you can check the result of evaluation in the **"Outputs"** tab of the batch run detail panel. You need select the new evaluation run to view its result.
+After the evaluation run completed, similarly, you can check the result of evaluation in the **"Outputs"** tab of the batch run detail panel. You need to select the new evaluation run to view its result.
 
 :::image type="content" source="./media/how-to-bulk-test-evaluate-flow/batch-run-detail-output-new-evaluation.png" alt-text="Screenshot of batch run detail page on the output tab with checking the new evaluation output." lightbox = "./media/how-to-bulk-test-evaluate-flow/batch-run-detail-output-new-evaluation.png":::
 
@@ -182,7 +183,7 @@ System message, sometimes referred to as a metaprompt or [system prompt](../../c
 
 ## Further reading: Guidance for creating Golden Datasets used for Copilot quality assurance
 
-The creation of copilot that use Large Language Models (LLMs) typically involves grounding the model in reality using source datasets. However, to ensure that the LLMs provide the most accurate and useful responses to customer queries, a "Golden Dataset" is necessary.
+The creation of a copilot that use Large Language Models (LLMs) typically involves grounding the model in reality using source datasets. However, to ensure that the LLMs provide the most accurate and useful responses to customer queries, a "Golden Dataset" is necessary.
 
 A Golden Dataset is a collection of realistic customer questions and expertly crafted answers. It serves as a Quality Assurance tool for LLMs used by your copilot. Golden Datasets are not used to train an LLM or inject context into an LLM prompt. Instead, they are utilized to assess the quality of the answers generated by the LLM.
 
diff --git a/articles/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference.md b/articles/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference.md
@@ -315,6 +315,12 @@ Select **Metrics** tab in the left navigation. Select **promptflow standard metr
 
 ## Troubleshoot endpoints deployed from prompt flow
 
+### Lack authorization to perform action "Microsoft.MachineLearningService/workspaces/datastores/read"
+
+If your flow contains Index Look Up tool, after deploying the flow, the endpoint needs to access workspace datastore to read MLIndex yaml file or FAISS folder containing chunks and embeddings. Hence, you need to manually grant the endpoint identity permission to do so.
+
+You can either grant the endpoint identity **AzureML Data Scientist** on workspace scope, or a custom role which contains "MachineLearningService/workspace/datastore/reader" action.
+
 ### MissingDriverProgram Error
 
 If you deploy your flow with custom environment and encounter the following error, it might be because you didn't specify the `inference_config` in your custom environment definition.
@@ -335,7 +341,14 @@ If you deploy your flow with custom environment and encounter the following erro
 
 There are 2 ways to fix this error.
 
-1. You can fix this error by adding `inference_config` in your custom environment definition. Learn more about [how to use customized environment](#use-customized-environment).
+- (Recommended) You can find the container image uri in your custom environment detail page, and set it as the flow base image in the flow.dag.yaml file. When you deploy the flow in UI, you just select **Use environment of current flow definition**, and the backend service will create the customized environment based on this base image and `requirement.txt` for your deployment. Learn more about [the environment specified in the flow definition](#use-environment-of-current-flow-definition). 
+
+    :::image type="content" source="./media/how-to-deploy-for-real-time-inference/custom-environment-image-uri.png" alt-text="Screenshot of custom environment detail page. " lightbox = "./media/how-to-deploy-for-real-time-inference/custom-environment-image-uri.png":::
+
+    :::image type="content" source="./media/how-to-deploy-for-real-time-inference/flow-environment-image.png" alt-text="Screenshot of specifying base image in raw yaml file of the flow. " lightbox = "./media/how-to-deploy-for-real-time-inference/flow-environment-image.png":::
+
+
+- You can fix this error by adding `inference_config` in your custom environment definition. Learn more about [how to use customized environment](#use-customized-environment).
 
     Following is an example of customized environment definition.
 
@@ -358,12 +371,6 @@ inference_config:
     path: /score
 ```
 
-2. You can find the container image uri in your custom environment detail page, and set it as the flow base image in the flow.dag.yaml file. When you deploy the flow in UI, you just select **Use environment of current flow definition**, and the backend service will create the customized environment based on this base image and `requirement.txt` for your deployment. Learn more about [the environment specified in the flow definition](#use-environment-of-current-flow-definition). 
-
-    :::image type="content" source="./media/how-to-deploy-for-real-time-inference/custom-environment-image-uri.png" alt-text="Screenshot of custom environment detail page. " lightbox = "./media/how-to-deploy-for-real-time-inference/custom-environment-image-uri.png":::
-
-    :::image type="content" source="./media/how-to-deploy-for-real-time-inference/flow-environment-image.png" alt-text="Screenshot of specifying base image in raw yaml file of the flow. " lightbox = "./media/how-to-deploy-for-real-time-inference/flow-environment-image.png":::
-
 ### Model response taking too long
 
 Sometimes, you might notice that the deployment is taking too long to respond. There are several potential factors for this to occur. 
@@ -398,3 +405,4 @@ If you aren't going use the endpoint after completing this tutorial, you should
 
 - [Iterate and optimize your flow by tuning prompts using variants](how-to-tune-prompts-using-variants.md)
 - [View costs for an Azure Machine Learning managed online endpoint](../how-to-view-online-endpoints-costs.md)
+- [Troubleshoot prompt flow deployments.](how-to-troubleshoot-prompt-flow-deployment.md)
diff --git a/articles/machine-learning/prompt-flow/how-to-deploy-to-code.md b/articles/machine-learning/prompt-flow/how-to-deploy-to-code.md
@@ -462,5 +462,6 @@ request_settings:
 - Learn more about [managed online endpoint schema](../reference-yaml-endpoint-online.md) and [managed online deployment schema](../reference-yaml-deployment-managed-online.md).
 - Learn more about how to [test the endpoint in UI](./how-to-deploy-for-real-time-inference.md#test-the-endpoint-with-sample-data) and [monitor the endpoint](./how-to-deploy-for-real-time-inference.md#view-managed-online-endpoints-common-metrics-using-azure-monitor-optional).
 - Learn more about how to [troubleshoot managed online endpoints](../how-to-troubleshoot-online-endpoints.md).
+- [Troubleshoot prompt flow deployments.](how-to-troubleshoot-prompt-flow-deployment.md)
 - Once you improve your flow, and would like to deploy the improved version with safe rollout strategy, see [Safe rollout for online endpoints](../how-to-safely-rollout-online-endpoints.md).
 - Learn more about [deploy flows to other platforms, such as a local development service, Docker container, Azure APP service, etc.](https://microsoft.github.io/promptflow/how-to-guides/deploy-a-flow/index.html)
diff --git a/articles/machine-learning/prompt-flow/how-to-troubleshoot-prompt-flow-deployment.md b/articles/machine-learning/prompt-flow/how-to-troubleshoot-prompt-flow-deployment.md
diff --git a/articles/machine-learning/toc.yml b/articles/machine-learning/toc.yml
diff --git a/articles/operator-insights/operator-insights-faq.yml b/articles/operator-insights/operator-insights-faq.yml
diff --git a/articles/storage/blobs/storage-auth-abac.md b/articles/storage/blobs/storage-auth-abac.md
