Merge pull request #276019 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: Taojunshen

articles/ai-services/computer-vision/includes/quickstarts-sdk/identity-python-sdk.md

@@ -15,12 +15,12 @@ ms.author: pafarley
 
 Get started with facial recognition using the Face client library for Python. Follow these steps to install the package and try out the example code for basic tasks. The Face service provides you with access to advanced algorithms for detecting and recognizing human faces in images. Follow these steps to install the package and try out the example code for basic face identification using remote images.
 
-[Reference documentation](/python/api/azure-ai-vision-face/azure.ai.vision.face) | [Library source code](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/azure/ai/vision/face) | [Package (PiPy)](https://aka.ms/azsdk-python-face-pkg) | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples)
+[Reference documentation](/python/api/overview/azure/cognitiveservices/face-readme) | [Library source code](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/cognitiveservices/azure-cognitiveservices-vision-face) | [Package (PiPy)](https://pypi.org/project/azure-cognitiveservices-vision-face/) | [Samples](/samples/browse/?products=azure&term=face)
 
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services/)
-* [Python 3.8+](https://www.python.org/)
+* [Python 3.x](https://www.python.org/)
   * Your Python installation should include [pip](https://pip.pypa.io/en/stable/). You can check if you have pip installed by running `pip --version` on the command line. Get pip by installing the latest version of Python.
 * [!INCLUDE [contributor-requirement](../../../includes/quickstarts/contributor-requirement.md)]
 * Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesFace"  title="Create a Face resource"  target="_blank">create a Face resource</a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
@@ -38,7 +38,7 @@ Get started with facial recognition using the Face client library for Python. Fo
     After installing Python, you can install the client library with:
 
     ```console
-    python -m pip install azure-ai-vision-face
+    pip install --upgrade azure-cognitiveservices-vision-face
     ```
 
 1. Create a new Python application
@@ -65,28 +65,25 @@ Get started with facial recognition using the Face client library for Python. Fo
 ## Output
 
 ```console
-Person group: dbd92bf0-8b74-43fc-a27a-b127c1bb1b66
-face 1d09b50e-0fb6-430c-a47c-9bb235761c17 added to person ea92a5d5-5250-44db-88fb-3b32e1a1ecaf
-face 74e1807a-6c86-4c74-b497-a3bcdda8c631 added to person ea92a5d5-5250-44db-88fb-3b32e1a1ecaf
-face 512cc8ff-e18a-4702-9413-3c83af9a0915 added to person f03219b3-c2dc-4ad6-b00b-bd71792686ac
-face 899bbe8e-2d03-4941-8221-d087911df21b added to person f03219b3-c2dc-4ad6-b00b-bd71792686ac
-face dfc0d142-36b0-4d90-982b-b51570ead5a8 added to person 8697d263-be7b-4d78-ba40-b55305dbbeb6
-face 29939a66-9da2-46f2-b572-abbe4e0d754a added to person 8697d263-be7b-4d78-ba40-b55305dbbeb6
-Train the person group dbd92bf0-8b74-43fc-a27a-b127c1bb1b66
-The person group dbd92bf0-8b74-43fc-a27a-b127c1bb1b66 is trained successfully.
+Person group: c8e679eb-0b71-43b4-aa91-ab8200cae7df
+face 861d769b-d014-40e8-8b4a-7fd3bc9b425b added to person f80c1cfa-b8cb-46f8-9f7f-e72fbe402bc3
+face e3c356a4-1ac3-4c97-9219-14648997f195 added to person f80c1cfa-b8cb-46f8-9f7f-e72fbe402bc3
+face f9119820-c374-4c4d-b795-96ae2fec5069 added to person be4084a7-0c7b-4cf9-9463-3756d2e28e17
+face 67d626df-3f75-4801-9364-601b63c8296a added to person be4084a7-0c7b-4cf9-9463-3756d2e28e17
+face 19e2e8cc-5029-4087-bca0-9f94588fb850 added to person 3ff07c65-6193-4d3e-bf18-d7c106393cd5
+face dcc61e80-16b1-4241-ae3f-9721597bae4c added to person 3ff07c65-6193-4d3e-bf18-d7c106393cd5
+pg resource is c8e679eb-0b71-43b4-aa91-ab8200cae7df
+<msrest.pipeline.ClientRawResponse object at 0x00000240DAD47310>
+Training status: running.
+
+Training status: succeeded.
+
 Pausing for 10 seconds to avoid triggering rate limit on free account...
 Identifying faces in image
-Person is identified for face ID 5779a986-238c-499d-b22a-d2a7cec92e88 in image, with a confidence of 0.96725.
-verification result: True. confidence: 0.96725
-Person is identified for face ID a28a4997-600e-4595-be39-d7a7d0f8afc8 in image, with a confidence of 0.96921.
-verification result: True. confidence: 0.96921
-No person identified for face ID 02a56d35-f3a4-43eb-a295-f23a1b772de9 in image.
-Person is identified for face ID 5de2019a-c4d3-4021-b8d0-9a3b86adceb7 in image, with a confidence of 0.92886.
-verification result: True. confidence: 0.92886
-
-The person group dbd92bf0-8b74-43fc-a27a-b127c1bb1b66 is deleted.
-
-End of quickstart.
+Person for face ID 40582995-d3a8-41c4-a9d1-d17ae6b46c5c is identified in image, with a confidence of 0.96725.
+Person for face ID 7a0368a2-332c-4e7a-81c4-2db3d74c78c5 is identified in image, with a confidence of 0.96921.
+No person identified for face ID c4a3dd28-ef2d-457e-81d1-a447344242c4 in image.
+Person for face ID 360edf1a-1e8f-402d-aa96-1734d0c21c1c is identified in image, with a confidence of 0.92886.
 ```
 
 
@@ -98,6 +95,10 @@ If you want to clean up and remove an Azure AI services subscription, you can de
 * [Portal](../../../multi-service-resource.md?pivots=azportal#clean-up-resources)
 * [Azure CLI](../../../multi-service-resource.md?pivots=azcli#clean-up-resources)
 
+To delete the **PersonGroup** you created in this quickstart, run the following code in your script:
+
+[!code-python[](~/cognitive-services-quickstart-code/python/Face/FaceQuickstart.py?name=snippet_deletegroup)]
+
 ## Next steps
 
 In this quickstart, you learned how to use the Face client library for Python to do basic face identification. Next, learn about the different face detection models and how to specify the right model for your use case.
@@ -106,4 +107,4 @@ In this quickstart, you learned how to use the Face client library for Python to
 > [Specify a face detection model version](../../how-to/specify-detection-model.md)
 
 * [What is the Face service?](../../overview-identity.md)
-* More extensive sample code can be found on [GitHub](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples).
+* More extensive sample code can be found on [GitHub](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/Face/FaceQuickstart.py).

articles/aks/azure-cni-powered-by-cilium.md

@@ -73,7 +73,7 @@ Azure CNI powered by Cilium currently has the following limitations:
 Use the following commands to create a cluster with an overlay network and Cilium. Replace the values for `<clusterName>`, `<resourceGroupName>`, and `<location>`:
 
 ```azurecli-interactive
-az aks create --name <clusterName> --resource-group <resourceGroupName> --loctaion <location> \
+az aks create --name <clusterName> --resource-group <resourceGroupName> --location <location> \
   --network-plugin azure \
   --network-plugin-mode overlay \
   --pod-cidr 192.168.0.0/16 \

articles/azure-monitor/agents/troubleshooter-ama-linux.md

@@ -36,7 +36,7 @@ sudo python3 -V
 Multiple versions of Python can be installed and aliased – if multiple versions are installed, use:
 
 ```Bash
-ls -ls /usr/bing/python*
+ls -ls /usr/bin/python*
 ```
 
 :::image type="content" source="media/agent-linux/python-multiple-version-check.png" alt-text="Screenshot of command to check multiple versions of Python for Linux." lightbox="media/agent-linux/python-multiple-version-check.png":::

articles/container-apps/opentelemetry-agents.md

@@ -167,7 +167,7 @@ While you can set up as many OTLP-configured endpoints as you like, each endpoin
     "appInsightsConfiguration": {},
     "openTelemetryConfiguration": {
       "destinationsConfiguration":{
-        "otlpConfiguration": [
+        "otlpConfigurations": [
           {
             "name": "otlp1",
             "endpoint": "ENDPOINT_URL_1",

articles/event-hubs/log-compaction.md

@@ -15,7 +15,7 @@ Log compaction is a way of retaining data in Event Hubs using event key based re
 
 As shown in the following image, an event log (of an event hub partition) may have multiple events with the same key. If you're using a compacted event hub, then Event Hubs service takes care of purging old events and only keeping the latest events of a given event key. 
 
-:::image type="content" source="./media/event-hubs-log-compaction/log-compaction.png" alt-text="Diagram showing how a topic gets compacted." lightbox="./media/event-hubs-resource-governance-overview/app-groups.png":::
+:::image type="content" source="./media/event-hubs-log-compaction/log-compaction.png" alt-text="Diagram showing how a topic gets compacted." lightbox="./media/event-hubs-log-compaction/log-compaction.png":::
 
 ### Compaction key
 The partition key that you set with each event is used as the compaction key. 

articles/machine-learning/how-to-manage-hub-workspace-portal.md

@@ -102,7 +102,7 @@ At hub creation, select between the networking isolation modes: **Public**, **Pr
 
 At hub creation in the Azure portal, creation of associated Azure AI services, Storage account, Key vault, Application insights, and Container registry is given. These resources are found on the Resources tab during creation. 
 
-To connect to Azure AI services (Azure OpenAI, Azure AI Search, and Azure AI Content Safety) or storage accounts in Azure AI Studio, create a private endpoint in your virtual network. Ensure the public network access (PNA) flag is disabled when creating the private endpoint connection. For more about Azure AI services connections, see [Azure AI services and virtual networks]](../../ai-services/cognitive-services-virtual-networks.md). You can optionally bring your own (BYO) search, but this requires a private endpoint connection from your virtual network.
+To connect to Azure AI services (Azure OpenAI, Azure AI Search, and Azure AI Content Safety) or storage accounts in Azure AI Studio, create a private endpoint in your virtual network. Ensure the public network access (PNA) flag is disabled when creating the private endpoint connection. For more about Azure AI services connections, see [Azure AI services and virtual networks](../ai-services/cognitive-services-virtual-networks.md). You can optionally bring your own (BYO) search, but this requires a private endpoint connection from your virtual network.
 
 ### Encryption
 
@@ -123,4 +123,4 @@ You can configure your hub for these resources during creation or update after c
 
 ## Next steps
 
-Once you have a workspace hub, you can Create a project using [Azure Machine Learning studio](how-to-manage-workspace.md?tabs=mlstudio), [AI Studio](../ai-studio/how-to/create-projects.md), [Azure SDK](how-to-manage-workspace.md?tabs=python), or [Using automation templates](how-to-create-workspace-template.md).
+Once you have a workspace hub, you can Create a project using [Azure Machine Learning studio](how-to-manage-workspace.md?tabs=mlstudio), [AI Studio](../ai-studio/how-to/create-projects.md), [Azure SDK](how-to-manage-workspace.md?tabs=python), or [Using automation templates](how-to-create-workspace-template.md).

articles/site-recovery/site-recovery-monitor-and-troubleshoot.md

@@ -254,7 +254,7 @@ You can manage your alerts settings under **Business Continuity Center** > **Mon
 
 Select **View alert** to get alert details and take action. 
 
-:::image type="content" source="./media/site-recovery-monitor-and-troubleshoot/manage-alerts.png" alt-text="Screenshot displays Manage alerts option." lightbox="./media/site-recovery-monitor-and-troubleshoot/manage-alerts.png":::
+:::image type="content" source="./media/site-recovery-monitor-and-troubleshoot/manage-alerts.png" alt-text="Screenshot displays View alert option." lightbox="./media/site-recovery-monitor-and-troubleshoot/manage-alerts.png":::
 
 Like Azure Monitor, Business Continuity Center, and Recovery Services Vault you can view alerts from Backup Center as well. 
 

articles/storage/common/multiple-identity-scenarios.md

@@ -318,7 +318,7 @@ CosmosClient client = new CosmosClient(
     });
 
 // Open a connection to Azure SQL using a managed identity
-string ConnectionString1 = @"Server=<azure-sql-hostname>.database.windows.net; User Id=ObjectIdOfManagedIdentity; Authentication=Active Directory Default; Database=<database-name>";
+string ConnectionString1 = @"Server=<azure-sql-hostname>.database.windows.net; User Id=ClientIDOfTheManagedIdentity; Authentication=Active Directory Default; Database=<database-name>";
 
 using (SqlConnection conn = new SqlConnection(ConnectionString1))
 {

articles/synapse-analytics/spark/tutorial-spark-pool-filesystem-spec.md

@@ -105,10 +105,9 @@ FSSPEC can read/write ADLS data by specifying the linked service name.
    import fsspec
    import pandas
    
-   adls_account_name = '' #Provide exact ADLS account name
-   sas_key = TokenLibrary.getConnectionString(<LinkedServiceName>)
+   linked_service_name = '' #Provide exact Linked Service Name
    
-   fsspec_handle = fsspec.open('abfs[s]://<container>/<path-to-file>', account_name =    adls_account_name, sas_token=sas_key)
+   fsspec_handle = fsspec.open('abfs[s]://<container>/<path-to-file>', storage_options={'linked_service': linked_service_name})
    
    with fsspec_handle.open() as f:
        df = pandas.read_csv(f)
@@ -117,17 +116,16 @@ FSSPEC can read/write ADLS data by specifying the linked service name.
    import fsspec
    import pandas
    
-   adls_account_name = '' #Provide exact ADLS account name
+   linked_service_name = '' #Provide exact Linked Service Name
    
    data = pandas.DataFrame({'Name':['Tom', 'nick', 'krish', 'jack'], 'Age':[20, 21, 19, 18]})
-   sas_key = TokenLibrary.getConnectionString(<LinkedServiceName>) 
    
-   fsspec_handle = fsspec.open('abfs[s]://<container>/<path-to-file>', account_name =    adls_account_name, sas_token=sas_key, mode="wt") 
+   fsspec_handle = fsspec.open('abfs[s]://<container>/<path-to-file>', storage_options={'linked_service': linked_service_name}, mode="wt") 
    
    with fsspec_handle.open() as f:
        data.to_csv(f) 
    ```
-
+   
 ## Upload file from local file system to default ADLS storage account of Synapse workspace
 
 FSSPEC can upload a file from the local file system to a Synapse workspace default ADLS storage account.

includes/functions-opentelemetry-preview-note.md

@@ -6,4 +6,4 @@ ms.date: 05/17/2024
 ms.author: glenga
 ---
 > [!IMPORTANT]  
-> OpenTelemery support for Azure Functions is currently in preview, and your app must be hosted in a [Flex Consumption plan](../articles/azure-functions/flex-consumption-plan.md) to use OpenTelemetry. 
+> OpenTelemetry support for Azure Functions is currently in preview, and your app must be hosted in a [Flex Consumption plan](../articles/azure-functions/flex-consumption-plan.md) to use OpenTelemetry.