Merge pull request #280873 from MicrosoftDocs/repo_sync_working_branch

Taojunshen · web-flow · commit ac8731782294 · 2024-07-16T05:35:44.000+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/language-service/question-answering/quickstart/sdk.md b/articles/ai-services/language-service/question-answering/quickstart/sdk.md
@@ -16,7 +16,7 @@ zone_pivot_groups: custom-qna-quickstart
 # Quickstart: custom question answering
 
 > [!NOTE]
-> [Azure Open AI On Your Data](../../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure Open AI On Your Data, please check out our [guide](../how-to/azure-openai-integration.md).
+> [Azure OpenAI On Your Data](../../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure OpenAI On Your Data, please check out our [guide](../how-to/azure-openai-integration.md).
 
 > [!NOTE]
 > Are you looking to migrate your workloads from QnA Maker? See our [migration guide](../how-to/migrate-qnamaker-to-question-answering.md) for information on feature comparisons and migration steps.
diff --git a/articles/ai-services/qnamaker/Quickstarts/add-question-metadata-portal.md b/articles/ai-services/qnamaker/Quickstarts/add-question-metadata-portal.md
@@ -14,7 +14,7 @@ ms.custom: mode-ui
 # Add questions and answer with QnA Maker portal
 
 > [!NOTE]
-> [Azure Open AI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure Open AI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
+> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure OpenAI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
 
 Once a knowledge base is created, add question and answer (QnA) pairs with metadata to filter the answer. The questions in the following table are about Azure service limits, but each has to do with a different Azure search service.
 
diff --git a/articles/ai-services/qnamaker/Quickstarts/create-publish-knowledge-base.md b/articles/ai-services/qnamaker/Quickstarts/create-publish-knowledge-base.md
@@ -14,7 +14,7 @@ ms.custom: mode-other
 # Quickstart: Create, train, and publish your QnA Maker knowledge base
 
 > [!NOTE]
-> [Azure Open AI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure Open AI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
+> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure OpenAI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
 
 [!INCLUDE [Custom question answering](../includes/new-version.md)]
 
diff --git a/articles/ai-services/qnamaker/Quickstarts/get-answer-from-knowledge-base-using-url-tool.md b/articles/ai-services/qnamaker/Quickstarts/get-answer-from-knowledge-base-using-url-tool.md
@@ -16,7 +16,7 @@ ms.date: 01/19/2024
 # Get an answer from a QNA Maker knowledge base
 
 > [!NOTE]
-> [Azure Open AI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure Open AI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
+> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure OpenAI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
 
 [!INCLUDE [Custom question answering](../includes/new-version.md)]
 
diff --git a/articles/ai-services/qnamaker/Quickstarts/quickstart-sdk.md b/articles/ai-services/qnamaker/Quickstarts/quickstart-sdk.md
@@ -16,7 +16,7 @@ zone_pivot_groups: qnamaker-quickstart
 # Quickstart: QnA Maker client library
 
 > [!NOTE]
-> [Azure Open AI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure Open AI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
+> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure OpenAI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
 
 Get started with the QnA Maker client library. Follow these steps to install the package and try out the example code for basic tasks.
 
diff --git a/articles/azure-resource-manager/management/tag-resources.md b/articles/azure-resource-manager/management/tag-resources.md
@@ -20,7 +20,7 @@ This article describes the conditions and limitations for using tags. For steps
 
 ## Tag usage and recommendations
 
-You can apply tags to your Azure resources, resource groups, and subscriptions.
+You can apply tags to your Azure resources, resource groups, and subscriptions, but not to management groups.
 
 For recommendations on how to implement a tagging strategy, see [Resource naming and tagging decision guide](/azure/cloud-adoption-framework/decision-guides/resource-tagging/?toc=/azure/azure-resource-manager/management/toc.json).
 
diff --git a/articles/frontdoor/routing-methods.md b/articles/frontdoor/routing-methods.md
@@ -96,7 +96,7 @@ The weighted method enables some useful scenarios:
 
 ## <a name = "affinity"></a>Session affinity
 
-By default, without session affinity, Azure Front Door forwards requests originating from the same client to different origins. Certain stateful applications or in certain scenarios when ensuing requests from the same user prefers the same origin to process the initial request. The cookie-based session affinity feature is useful when you want to keep a user session on the same origin. When you use managed cookies with SHA256 of the origin URL as the identifier in the cookie, Azure Front Door can direct ensuing traffic from a user session to the same origin for processing.
+By default, without session affinity, Azure Front Door forwards requests originating from the same client to different origins. Certain stateful applications or in certain scenarios when ensuing requests from the same user prefers the same origin to process the initial request. The cookie-based session affinity feature is useful when you want to keep a user session on the same origin, such as scenarios where clients authenticate to the origin. When you use managed cookies with SHA256 of the origin URL as the identifier in the cookie, Azure Front Door can direct ensuing traffic from a user session to the same origin for processing.
 
 Session affinity can be enabled the origin group level in Azure Front Door Standard and Premium tier and front end host level in Azure Front Door (classic) for each of your configured domains (or subdomains). Once enabled, Azure Front Door adds a cookie to the user's session. The cookies are called ASLBSA and ASLBSACORS. Cookie-based session affinity allows Front Door to identify different users even if behind the same IP address, which in turn allows a more even distribution of traffic between your different origins.
 
diff --git a/articles/machine-learning/how-to-managed-network.md b/articles/machine-learning/how-to-managed-network.md
@@ -1113,6 +1113,8 @@ The Azure Machine Learning managed VNet feature is free. However, you're charged
 * FQDN outbound rules only support ports 80 and 443.
 * If your compute instance is in a managed network and is configured for no public IP, use the `az ml compute connect-ssh` command to connect to it using SSH.
 * When using Managed Vnet, you can't deploy compute resources inside your custom Vnet. Compute resources can only be created inside the managed Vnet.
+* Managed network isolation cannot establish a private connection from the managed virtual network to a user's on-premises resources.
+For the list of supported private connections, see [Private Endpoints](/azure/machine-learning/how-to-managed-network?view=azureml-api-2&tabs=azure-cli&preserve-view=true#private-endpoints).
 
 ### Migration of compute resources
 
diff --git a/articles/sentinel/data-connectors/microsoft-powerbi.md b/articles/sentinel/data-connectors/microsoft-powerbi.md
@@ -1,6 +1,6 @@
 ---
-title: "Microsoft PowerBI connector for Microsoft Sentinel"
-description: "Learn how to install the connector Microsoft PowerBI to connect your data source to Microsoft Sentinel."
+title: "Microsoft Power BI connector for Microsoft Sentinel"
+description: "Learn how to install the connector Microsoft Power BI to connect your data source to Microsoft Sentinel."
 author: cwatson-cat
 ms.topic: how-to
 ms.date: 04/26/2024
@@ -9,9 +9,9 @@ ms.author: cwatson
 ms.collection: sentinel-data-connector
 ---
 
-# Microsoft PowerBI connector for Microsoft Sentinel
+# Microsoft Power BI connector for Microsoft Sentinel
 
-Microsoft PowerBI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet, a collection of cloud-based and on-premises hybrid data warehouses, or a data store of some other type. This connector lets you stream PowerBI audit logs into Microsoft Sentinel, allowing you to track user activities in your PowerBI environment. You can filter the audit data by date range, user, dashboard, report, dataset, and activity type.
+Microsoft Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet, a collection of cloud-based and on-premises hybrid data warehouses, or a data store of some other type. This connector lets you stream Power BI audit logs into Microsoft Sentinel, allowing you to track user activities in your Power BI environment. You can filter the audit data by date range, user, dashboard, report, dataset, and activity type.
 
 This is autogenerated content. For changes, contact the solution provider.
 
diff --git a/articles/virtual-machines/linux/n-series-driver-setup.md b/articles/virtual-machines/linux/n-series-driver-setup.md
@@ -62,24 +62,29 @@ Ubuntu packages NVIDIA proprietary drivers. Those drivers come directly from NVI
    Reboot the VM after the GPU driver is installed.
 3. Download and install the CUDA toolkit from NVIDIA:
     > [!NOTE]
-   >  The example shows the CUDA package path for Ubuntu 22.04 LTS. Replace the path specific to the version you plan to use.
+   >  The example shows the CUDA package path for Ubuntu 24.04 LTS. Replace the path specific to the version you plan to use.
    >
-   >  Visit the [NVIDIA Download Center](https://developer.download.nvidia.com/compute/cuda/repos/) or the [NVIDIA CUDA Resources page](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=deb_network) for the full path specific to each version.
+   >  Visit the [NVIDIA Download Center](https://developer.download.nvidia.com/compute/cuda/repos/) or the [NVIDIA CUDA Resources page](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=24.04&target_type=deb_network) for the full path specific to each version.
    >
    ```bash
-   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
+   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
    sudo apt install -y ./cuda-keyring_1.1-1_all.deb
    sudo apt update
-   sudo apt -y install cuda-toolkit-12-3
+   sudo apt -y install cuda-toolkit-12-5
    ```
 
    The installation can take several minutes.
 
-4. Verify that the GPU is correctly recognized (you may need to reboot your VM for system changes to take effect):
+4. Reboot the VM after installation completes:
    ```bash
-   nvidia-smi
+   sudo reboot
    ```
 
+5. Verify that the GPU is correctly recognized (after reboot):
+   ```bash
+   nvidia-smi
+   ```
+   
 #### NVIDIA driver updates
 
 We recommend that you periodically update NVIDIA drivers after deployment.
