Merge pull request #283696 from MicrosoftDocs/repo_sync_working_branch

Taojunshen · web-flow · commit 8a86fe4f836e · 2024-08-07T00:32:12.000+08:00
Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)
diff --git a/articles/ai-services/content-safety/how-to/encrypt-data-at-rest.md b/articles/ai-services/content-safety/how-to/encrypt-data-at-rest.md
@@ -20,14 +20,10 @@ Azure AI Content Safety automatically encrypts your data when it's persisted to
 
 Azure AI Content Safety is part of Azure AI services. Azure AI services data is encrypted and decrypted using [FIPS 140-2](https://en.wikipedia.org/wiki/FIPS_140-2) compliant [256-bit AES](https://en.wikipedia.org/wiki/Advanced_Encryption_Standard) encryption. Encryption and decryption are transparent, meaning encryption and access are managed for you. Your data is secure by default and you don't need to modify your code or applications to take advantage of encryption.
 
-
 ## About encryption key management
 
 By default, your subscription uses Microsoft-managed encryption keys. There's also the option to manage your subscription with your own keys called customer-managed keys (CMK). CMK offers greater flexibility to create, rotate, disable, and revoke access controls. You can also audit the encryption keys used to protect your data.
 
-> [!IMPORTANT]
-> For blocklist names, only MMK encryption is applied by default. Using CMK or not will not change this behavior. All the other data will use either MMK or CMK depending on what you've selected.
-
 ## Customer-managed keys with Azure Key Vault
 
 Customer-managed keys (CMK), also known as Bring your own key (BYOK), offer greater flexibility to create, rotate, disable, and revoke access controls. You can also audit the encryption keys used to protect your data.
diff --git a/articles/ai-studio/how-to/model-catalog-overview.md b/articles/ai-studio/how-to/model-catalog-overview.md
@@ -18,7 +18,7 @@ author: ssalgadodev
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
-The model catalog in Azure AI studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models across model providers such as Azure OpenAI service, Mistral, Meta, Cohere, Nvidia, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.   
+The model catalog in Azure AI studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models across model providers such as Azure OpenAI Service, Mistral, Meta, Cohere, Nvidia, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.   
 
 ## Model Collections 
 
diff --git a/articles/api-management/azure-openai-api-from-specification.md b/articles/api-management/azure-openai-api-from-specification.md
@@ -22,16 +22,14 @@ This article shows two options to import an [Azure OpenAI Service](/azure/ai-ser
 ## Prerequisites
 
 - An existing API Management instance. [Create one if you haven't already](get-started-create-service-instance.md).
-- Access granted to Azure OpenAI in the desired Azure subscription.
-    You can apply for access to Azure OpenAI by completing the form at https://aka.ms/oai/access. Open an issue on this repo to contact us if you have an issue.
 - An Azure OpenAI resource with a model deployed. For more information about model deployment, see the [resource deployment guide](../ai-services/openai/how-to/create-resource.md).
 
     Make a note of the ID (name) of the deployment. You'll need it when you test the imported API in API Management.
 - Permissions to grant access to the Azure OpenAI resource from the API Management instance.
 
 ## Option 1. Import API from Azure OpenAI Service
 
-You can import an Azure OpenAI API directly to API Management from the Azure OpenAI Service. 
+You can import an Azure OpenAI API directly from Azure OpenAI Service to API Management. 
 
 [!INCLUDE [api-management-workspace-availability](../../includes/api-management-workspace-availability.md)]
 
diff --git a/articles/azure-monitor/vm/vminsights-dependency-agent-maintenance.md b/articles/azure-monitor/vm/vminsights-dependency-agent-maintenance.md
@@ -75,7 +75,7 @@ You can download the latest version of the Linux agent from [here](https://aka.m
 2. Run the following command as root.
 
     ```bash
-    InstallDependencyAgent-Linux64.bin -s
+    ./InstallDependencyAgent-Linux64.bin -s
     ```
 
 If the Dependency agent fails to start, check the logs for detailed error information. On Linux agents, the log directory is */var/opt/microsoft/dependency-agent/log*. 
diff --git a/articles/backup/backup-azure-arm-vms-prepare.md b/articles/backup/backup-azure-arm-vms-prepare.md
@@ -91,7 +91,8 @@ To apply a backup policy to your Azure VMs, follow these steps:
      !["Select virtual machines" pane](./media/backup-azure-arm-vms-prepare/select-vms-to-backup.png)
 
     >[!NOTE]
-    > All the VMs in the same region and subscription as that of the vault are available to configure backup. When configuring backup, you can browse to the virtual machine name and its resource group, even though you don’t have the required permission on those VMs. If your VM is in soft deleted state, then it won't be visible in this list. If you need to re-protect the VM, then you need to wait for the soft delete period to expire or undelete the VM from the soft deleted list. For more information, see [the soft delete for VMs article](soft-delete-virtual-machines.md#soft-delete-for-vms-using-azure-portal).
+    >- All the VMs in the same region and subscription as that of the vault are available to configure backup. When configuring backup, you can browse to the virtual machine name and its resource group, even though you don’t have the required permission on those VMs. If your VM is in soft deleted state, then it won't be visible in this list. If you need to re-protect the VM, then you need to wait for the soft delete period to expire or undelete the VM from the soft deleted list. For more information, see [the soft delete for VMs article](soft-delete-virtual-machines.md#soft-delete-for-vms-using-azure-portal).
+    >- To change Recovery Services vault of a VM, firstly you need to stop the backup then you can assign a new vault to the VM.
 
 1. In **Backup**, select **Enable backup**. This deploys the policy to the vault and to the VMs, and installs the backup extension on the VM agent running on the Azure VM.
 
diff --git a/articles/backup/backup-azure-delete-vault.md b/articles/backup/backup-azure-delete-vault.md
@@ -65,6 +65,9 @@ To delete a vault, follow these steps:
      :::image type="content" source="./media/backup-azure-delete-vault/delete-items-in-soft-delete-state-inline.png" alt-text="Screenshot showing the process to delete items in soft-delete state." lightbox="./media/backup-azure-delete-vault/delete-items-in-soft-delete-state-expanded.png":::
 
   1. Go to the vault dashboard menu -> **Backup Items**. Click **Stop Backup** to stop the backups of all listed items, and then click **Delete Backup Data** to delete. [Follow these steps](#delete-protected-items-in-the-cloud) to remove those items.
+
+  >[!Note]
+  > You don't need to delete Virtual Machine or policy, you only need to stop backup to the vault.
       
 - <a id="portal-delete-backup-servers">**Step 5:**</a> Delete Backup Servers
 
diff --git a/articles/expressroute/provider-rate-limit.md b/articles/expressroute/provider-rate-limit.md
@@ -16,7 +16,7 @@ This article discusses how rate limiting works for ExpressRoute circuits created
 
 ## How does rate limiting work over an ExpressRoute circuit?
 
-An ExpressRoute circuit consists of two links that connects the Customer or Provider edge to the Microsoft Enterprise Edge (MSEE) routers. With a circuit bandwidth of 1 Gbps and traffic distributed evenly across both links, a maximum throughput of 2 Gbps (twice the 1 Gbps) can be achieved. However, rate limiting will restricts your throughput to the configured bandwidth if it is exceeded on either link. It is important to note that the excess 1 Gbps in this example serves as redundancy to prevent service disruptions during any link or device maintenance periods.
+An ExpressRoute circuit consists of two links that connects the Customer or Provider edge to the Microsoft Enterprise Edge (MSEE) routers. With a circuit bandwidth of 1 Gbps and traffic distributed evenly across both links, a maximum throughput of 2 Gbps (twice the 1 Gbps) can be achieved. However, rate limiting will restrict your throughput to the configured bandwidth if it is exceeded on either link. It is important to note that the excess 1 Gbps in this example serves as redundancy to prevent service disruptions during any link or device maintenance periods.
 
 :::image type="content" source="./media/provider-rate-limit/circuit.png" alt-text="Diagram of rate limiting on an ExpressRoute circuit over provider ports.":::
 
@@ -38,7 +38,7 @@ You can seamlessly increase your circuit bandwidth through the Azure portal. For
 
 ## What are the causes of traffic drop when the throughput is below the configured bandwidth?
 
-ExpressRoute circuit throughput is monitored at an aggregate level of every few minutes, while the rate limiting is enforced at a granular level in milliseconds. Therefore, occasional traffic bursts exceeding the configured bandwidth might not get detected by the throughput monitoring. However, the rate limiting is still be enforced and traffic gets dropped.
+ExpressRoute circuit throughput is monitored at an aggregate level of every few minutes, while the rate limiting is enforced at a granular level in milliseconds. Therefore, occasional traffic bursts exceeding the configured bandwidth might not get detected by the throughput monitoring. However, the rate limiting is still being enforced and traffic gets dropped.
 
 ## Next steps
 
diff --git a/articles/machine-learning/how-to-connection.md b/articles/machine-learning/how-to-connection.md
@@ -440,7 +440,7 @@ The following example creates a Python feed connection. A Personal Access Token
 
 ```python
 from azure.ai.ml.entities import WorkspaceConnection
-from azure.ai.ml.entities import UsernamePasswordConfiguration, ManagedIdentityConfiguration  
+from azure.ai.ml.entities import UsernamePasswordConfiguration, PatTokenConfiguration
 
 
 name = "my_pfeed_conn"
@@ -465,24 +465,12 @@ You can't create a Python feed connection in studio.
 
 ---
 
-### Azure Container Registry
+### Container Registry
 
 # [Azure CLI](#tab/cli)
 
 Create a connection to an Azure Container Registry with one of following YAML files. Be sure to update the appropriate values:
 
-* Connect using Microsoft Entra ID authentication:
-
-   ```yml
-   name: test_ws_conn_cr_managed
-   type: container_registry
-   target: https://test-feed.com
-   credentials:
-      type: managed_identity
-      client_id: client_id
-      resource_id: resource_id
-   ```
-
 * Connect using a username and password:
 
    ```yml
@@ -507,7 +495,7 @@ The following example creates an Azure Container Registry connection. A managed
 
 ```python
 from azure.ai.ml.entities import WorkspaceConnection
-from azure.ai.ml.entities import UsernamePasswordConfiguration, PatTokenConfiguration  
+from azure.ai.ml.entities import UsernamePasswordConfiguration
 
 
 name = "my_acr_conn"
@@ -518,7 +506,7 @@ wps_connection = WorkspaceConnection(
     name=name,
     type="container_registry",
     target=target,
-    credentials=ManagedIdentityConfiguration (client_id="xxxxx", resource_id="xxxxx"),    
+    credentials=UsernamePasswordConfiguration(username="xxxxx", password="xxxxx"), 
 )
 ml_client.connections.create_or_update(workspace_connection=wps_connection)
 ```
@@ -556,4 +544,4 @@ ml_client.connections.create_or_update(workspace_connection=wps_connection)
 If you use a data connection (Snowflake DB, Amazon S3, or Azure SQL DB), these articles offer more information:
 
 - [Import data assets](how-to-import-data-assets.md)
-- [Schedule data import jobs](how-to-schedule-data-import.md)
+- [Schedule data import jobs](how-to-schedule-data-import.md)
