Merge pull request #260154 from MicrosoftDocs/repo_sync_working_branch

rmca14 · web-flow · commit 95805e2c7837 · 2023-12-04T10:21:07.000-08:00
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/openai/how-to/switching-endpoints.md b/articles/ai-services/openai/how-to/switching-endpoints.md
@@ -52,7 +52,7 @@ client = AzureOpenAI(
     api_key=os.getenv("AZURE_OPENAI_KEY"),  
     api_version="2023-10-01-preview",
     azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
-    )
+)
 ```
 
 </td>
diff --git a/articles/aks/open-ai-quickstart.md b/articles/aks/open-ai-quickstart.md
@@ -210,7 +210,7 @@ Now that the application is deployed, you can deploy the Python-based microservi
           nodeSelector:
             "kubernetes.io/os": linux
           containers:
-          - name: order-service
+          - name: ai-service
             image: ghcr.io/azure-samples/aks-store-demo/ai-service:latest
             ports:
             - containerPort: 5001
diff --git a/articles/azure-monitor/getting-started.md b/articles/azure-monitor/getting-started.md
@@ -21,7 +21,7 @@ To enable Azure Monitor to monitor all of your Azure resources, you need to both
 - Configure Azure resources to generate monitoring data for Azure Monitor to collect.
 
 > [!IMPORTANT]
-> If you're new to Azure Monitor or are want to monitor a single Azure resource, start with the [Monitor Azure resources with Azure Monitor tutorial](essentials/monitor-azure-resource.md). The tutorial provides general concepts for Azure Monitor and guidance for monitoring a single Azure resource. This article provides recommendations for preparing your environment to leverage all features of Azure Monitor to monitoring your entire set of applications and resources together at scale.
+> If you're new to Azure Monitor or want to monitor a single Azure resource, start with the [Monitor Azure resources with Azure Monitor tutorial](essentials/monitor-azure-resource.md). The tutorial provides general concepts for Azure Monitor and guidance for monitoring a single Azure resource. This article provides recommendations for preparing your environment to leverage all features of Azure Monitor to monitoring your entire set of applications and resources together at scale.
 
 ## Getting started workflow
 These articles provide detailed information about each of the main steps you'll need to do when getting started with Azure Monitor.
diff --git a/articles/azure-monitor/visualize/workbooks-automate.md b/articles/azure-monitor/visualize/workbooks-automate.md
@@ -301,9 +301,6 @@ In this example, the following steps facilitate the customization of an exported
 1. Use the new `reserializedData` variable in place of the original `serializedData` property.
 1. Deploy the new workbook resource by using the updated ARM template.
 
-### Limitations
-Currently, this mechanism can't be used to create workbook instances in the **Workbooks** gallery of Application Insights. We're working on addressing this limitation. In the meantime, we recommend that you use the **Troubleshooting Guides** gallery (workbookType: `tsg`) to deploy Application Insights-related workbooks.
-
 ## Next steps
 
 Explore how workbooks are being used to power the new [Storage insights experience](../../storage/common/storage-insights-overview.md?toc=%2fazure%2fazure-monitor%2ftoc.json).
diff --git a/articles/azure-monitor/visualize/workbooks-bring-your-own-storage.md b/articles/azure-monitor/visualize/workbooks-bring-your-own-storage.md
@@ -38,10 +38,6 @@ There are times when you might have a query or some business logic that you want
 
 - When you save to custom storage, you can't pin individual parts of the workbook to a dashboard because the individual pins would contain protected information in the dashboard itself. When you use custom storage, you can only pin links to the workbook itself to dashboards.
 - After a workbook has been saved to custom storage, it will always be saved to custom storage, and this feature can't be turned off. To save elsewhere, you can use **Save As** and elect to not save the copy to custom storage.
-- Workbooks in an Application Insights resource are "legacy" workbooks and don't support custom storage. The latest feature for workbooks in an Application Insights resource is the **More** selection. Legacy workbooks don't have **Subscription** options when you save them.
-
-   <!-- convertborder later -->
-   :::image type="content" source="./media/workbooks-bring-your-own-storage/legacy-workbooks.png" lightbox="./media/workbooks-bring-your-own-storage/legacy-workbooks.png" alt-text="Screenshot that shows a legacy workbook." border="false":::
 
 ## Next steps
 
diff --git a/articles/cosmos-db/mongodb/vcore/how-to-scale-cluster.md b/articles/cosmos-db/mongodb/vcore/how-to-scale-cluster.md
@@ -79,4 +79,4 @@ You can enable or disable high availability (HA) to suit your needs. HA avoids d
 In this guide, we've shown that scaling and configuring your Cosmos DB for MongoDB vCore cluster in the Azure portal is a straightforward process. The Azure portal includes the ability to adjust the cluster tier, increase storage size, and enable or disable high availability without any downtime.
 
 > [!div class="nextstepaction"]
-> [Restore a Azure Cosmos DB for MongoDB vCore cluster](how-to-restore-cluster.md)
+> [Restore an Azure Cosmos DB for MongoDB vCore cluster](how-to-restore-cluster.md)
diff --git a/articles/healthcare-apis/dicom/dicom-extended-query-tags-overview.md b/articles/healthcare-apis/dicom/dicom-extended-query-tags-overview.md
@@ -79,6 +79,7 @@ The following VR types are supported:
 > [!NOTE]
 > Sequential tags, which are tags under a tag of type Sequence of Items (SQ), are currently not supported.
 > You can add up to 128 extended query tags.
+> We do not index extended query tags if the value is null or empty.
 
 #### Responses
 
diff --git a/articles/healthcare-apis/dicom/dicom-services-conformance-statement-v2.md b/articles/healthcare-apis/dicom/dicom-services-conformance-statement-v2.md
@@ -86,7 +86,7 @@ The following DICOM elements are required to be present in every DICOM file atte
 * `PatientID`
 
 > [!NOTE]
-> All UIDs must be between 1 and 64 characters long, and only contain alpha numeric characters or the following special characters: `.`, `-`. `PatientID` is validated based on its `LO` `VR` type.
+> All UIDs must be between 1 and 64 characters long, and only contain alpha numeric characters or the following special characters: `.`, `-`. `PatientID` continues to be a required tag and can have the value as null in the input. `PatientID` is validated based on its `LO` `VR` type.
 
 Each file stored must have a unique combination of `StudyInstanceUID`, `SeriesInstanceUID`, and `SopInstanceUID`. The warning code `45070` is returned if a file with the same identifiers already exists.
 
@@ -462,6 +462,9 @@ We support searching the following attributes and search types.
 | `ManufacturerModelName` | | X | X | X | X |  |
 | `SOPInstanceUID` |  |  | X |  | X | X |
 
+> [!NOTE]
+> We do not support searching using empty string for any attributes.
+
 #### Search matching
 
 We support the following matching types.
@@ -893,6 +896,9 @@ We support searching on these attributes:
 |`ProcedureStepState`|
 |`StudyInstanceUID`|
 
+> [!NOTE]
+> We do not support searching using empty string for any attributes.
+
 ##### Search Matching
 
 We support these matching types:
diff --git a/articles/machine-learning/concept-foundation-models.md b/articles/machine-learning/concept-foundation-models.md
@@ -69,6 +69,6 @@ Support|Supported by Microsoft and covered by [Azure Machine Learning SLA](https
 ## Learn more
 
 Learn [how to use Foundation Models in Azure Machine Learning](./how-to-use-foundation-models.md) for fine-tuning, evaluation and deployment using Azure Machine Learning studio UI or code based methods.
-* Explore the [model catalog in Azure Machine Learning studio](https://ml.azure.com/model/catalog). You need a [Azure Machine Learning workspace](./quickstart-create-resources.md) to explore the catalog.
+* Explore the [model catalog in Azure Machine Learning studio](https://ml.azure.com/model/catalog). You need an [Azure Machine Learning workspace](./quickstart-create-resources.md) to explore the catalog.
 * [Evaluate, fine-tune and deploy models](./how-to-use-foundation-models.md) curated by Azure Machine Learning.
 
diff --git a/articles/machine-learning/v1/how-to-select-algorithms.md b/articles/machine-learning/v1/how-to-select-algorithms.md
@@ -33,7 +33,7 @@ A common question is “Which machine learning algorithm should I use?” The al
 >This article applies to classic prebuilt components and not compatible with CLI v2 and SDK v2.
 ## Business scenarios and the Machine Learning Algorithm Cheat Sheet
 
-The [Azure Machine Learning Algorithm Cheat Sheet](./algorithm-cheat-sheet.md?WT.mc_id=docs-article-lazzeri) helps you with the first consideration: **What you want to do with your data**? On the Machine Learning Algorithm Cheat Sheet, look for task you want to do, and then find a [Azure Machine Learning designer](./concept-designer.md?WT.mc_id=docs-article-lazzeri) algorithm for the predictive analytics solution. 
+The [Azure Machine Learning Algorithm Cheat Sheet](./algorithm-cheat-sheet.md?WT.mc_id=docs-article-lazzeri) helps you with the first consideration: **What you want to do with your data**? On the Machine Learning Algorithm Cheat Sheet, look for task you want to do, and then find an [Azure Machine Learning designer](./concept-designer.md?WT.mc_id=docs-article-lazzeri) algorithm for the predictive analytics solution. 
 
 Machine Learning designer provides a comprehensive portfolio of algorithms, such as [Multiclass Decision Forest](../algorithm-module-reference/multiclass-decision-forest.md?WT.mc_id=docs-article-lazzeri), [Recommendation systems](../algorithm-module-reference/evaluate-recommender.md?WT.mc_id=docs-article-lazzeri), [Neural Network Regression](../algorithm-module-reference/neural-network-regression.md?WT.mc_id=docs-article-lazzeri), [Multiclass Neural Network](../algorithm-module-reference/multiclass-neural-network.md?WT.mc_id=docs-article-lazzeri), and [K-Means Clustering](../algorithm-module-reference/k-means-clustering.md?WT.mc_id=docs-article-lazzeri). Each algorithm is designed to address a different type of machine learning problem. See the [Machine Learning designer algorithm and component reference](../component-reference/component-reference.md?WT.mc_id=docs-article-lazzeri) for a complete list along with documentation about how each algorithm works and how to tune parameters to optimize the algorithm.
 
diff --git a/articles/mysql/migrate/how-to-migrate-single-flexible-minimum-downtime.md b/articles/mysql/migrate/how-to-migrate-single-flexible-minimum-downtime.md
@@ -157,7 +157,7 @@ To configure Data in replication, perform the following steps:
 
     - If SSL enforcement is enabled, then:
 
-        i. Download the certificate needed to communicate over SSL with your Azure Database for MySQL server from [here](https://www.digicert.com/CACerts/BaltimoreCyberTrustRoot.crt.pem).
+        i. Download the certificate needed to communicate over SSL with your Azure Database for MySQL server from [here](https://cacerts.digicert.com/DigiCertGlobalRootG2.crt.pem).
 
         ii. Open the file in notepad and paste the contents to the section “PLACE YOUR PUBLIC KEY CERTIFICATE'S CONTEXT HERE“.
 
diff --git a/articles/mysql/single-server/how-to-troubleshoot-high-cpu-utilization.md b/articles/mysql/single-server/how-to-troubleshoot-high-cpu-utilization.md
@@ -21,6 +21,32 @@ For example, consider a sudden surge in connections that initiates surge of data
 
 Besides capturing metrics, it’s important to also trace the workload to understand if one or more queries are causing the spike in CPU utilization.
 
+## High CPU causes
+
+CPU spikes can occur for various reasons, primarily due to spikes in connections and poorly written SQL queries, or a combination of both:
+
+#### Spike in connections
+
+An increase in connections can lead to an increase in threads, which in turn can cause a rise in CPU usage as it has to manage these connections along with their queries and resources. To troubleshoot a spike in connections, you should check the [Total Connections](./../flexible-server/concepts-monitoring.md#list-of-metrics) metric and refer to the next section for more details about these connections. You can utilize the performance_schema to identify the hosts and users currently connected to the server with the following commands:
+
+Current connected hosts
+```
+   select HOST,CURRENT_CONNECTIONS From performance_schema.hosts
+   where CURRENT_CONNECTIONS > 0
+   and host not in ('NULL','localhost');
+```
+
+Current connected users
+```
+   select USER,CURRENT_CONNECTIONS from performance_schema.users
+   where CURRENT_CONNECTIONS >0
+   and USER not in ('NULL','azure_superuser');
+```
+
+#### Poorly written SQL queries
+
+Queries that are expensive to execute and scan a large number of rows without an index, or those that perform temporary sorts along with other inefficient plans, can lead to CPU spikes. While some queries may execute quickly in a single session, they can cause CPU spikes when run in multiple sessions. Therefore, it’s crucial to always explain your queries that you capture from the [show processlist](https://dev.mysql.com/doc/refman/5.7/en/show-processlist.html) and ensure their execution plans are efficient. This can be achieved by ensuring they scan a minimal number of rows by using filters/where clause, utilize indexes and avoid using large temporary sort along with other bad execution plans. For more information about execution plans, see [EXPLAIN Output Format](https://dev.mysql.com/doc/refman/5.7/en/explain-output.html).
+
 ## Capturing details of the current workload
 
 The SHOW (FULL) PROCESSLIST command displays a list of all user sessions currently connected to the Azure Database for MySQL server. It also provides details about the current state and activity of each session.
