Merge pull request #2423 from MicrosoftDocs/repo_sync_working_branch

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

Author: Taojunshen

articles/ai-studio/how-to/flow-develop.md

@@ -34,7 +34,7 @@ In this article, you learn how to create and develop your first prompt flow in A
 - If you don't have an Azure AI Foundry project already, first [create a project](create-projects.md).
 - Prompt flow requires a compute session. If you don't have a runtime, you can [create one in Azure AI Foundry portal](./create-manage-compute-session.md).
 - You need a deployed model.
-- In your project, configure access control for the blog storage account. Assign the **Storage Blob Data Contributor** role to your user account.
+- In your project, configure access control for the blob storage account. Assign the **Storage Blob Data Contributor** role to your user account.
     - In the bottom left of the Azure AI Foundry portal, select **Management center**.
     - In **Connected resources** for your project, select the link that corresponds to the **Azure Blob Storage** type. 
     - Select **View in Azure Portal**

articles/search/search-howto-index-mysql.md

@@ -15,22 +15,22 @@ ms.custom:
 ms.date: 12/10/2024
 ---
 
-# Index data from Azure Database for MySQL flexible server
+# Index data from Azure Database for MySQL Flexible Server
 
 > [!IMPORTANT]
 > MySQL support is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). You can use 2020-06-30-preview or later to index your content. We recommend the latest preview API. There is currently no portal support.
 
 In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Database for MySQL and makes it searchable in Azure AI Search. Inputs to the indexer are your row, in a single table or view. Output is a search index with searchable content in individual fields.
 
-This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from Azure Database for MySQL flexible server. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
+This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from Azure Database for MySQL Flexible Server. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
 When configured to include a high water mark and soft deletion, the indexer takes all changes, uploads, and deletes for your MySQL database. It reflects these changes in your search index. Data extraction occurs when you submit the Create Indexer request.
 
 ## Prerequisites
 
 - [Register for the preview](https://aka.ms/azure-cognitive-search/indexer-preview) to provide scenario feedback. You can access the feature automatically after form submission.
 
-- [Azure Database for MySQL flexible server](/azure/mysql/flexible-server/overview) and sample data. Data must reside in a table or view. A primary key is required. If you're using a view, it must have a [high water mark column](#DataChangeDetectionPolicy). 
+- [Azure Database for MySQL Flexible Server](/azure/mysql/flexible-server/overview) and sample data. Data must reside in a table or view. A primary key is required. If you're using a view, it must have a [high water mark column](#DataChangeDetectionPolicy). 
 
 - Read permissions. A *full access* connection string includes a key that grants access to the content, but if you're using Azure roles, make sure the [search service managed identity](search-howto-managed-identities-data-sources.md) has **Reader** permissions on MySQL.