Merge pull request #3127 from MicrosoftDocs/main639186270163019092sync_temp

For protected branch, push strategy should use PR and merge to target branch method to work around git push error

Author: learn-build-service-prod[bot]

docs/data-factory/workspace-monitoring.md

@@ -27,6 +27,10 @@ Workspace monitoring gives you log-level visibility for all items in a workspace
 
     :::image type="content" source="media/monitor-pipeline-runs/pipeline-workspace-monitoring.png" alt-text="Screenshot of the pipeline workspace monitoring table." lightbox="media/monitor-pipeline-runs/pipeline-workspace-monitoring.png":::
 
+## Troubleshooting missing monitoring tables
+
+When new workspace monitoring tables become available and you don't see them in your existing monitoring eventhouse, recreate workspace monitoring for the workspace by deleting the previous eventhouse and re-adding it to your workspace. Recreating the monitoring eventhouse provisions the latest monitoring schema and ensures newly available tables are visible. Be sure to save any custom Kql queries before removing the existing monitoring eventhouse. The monitoring experience is backed by a monitoring eventhouse and monitoring Kql database that are created when workspace monitoring is enabled.
+
 ## Review logged data
 
 The **ItemJobEventLogs** table includes pipeline-level (L1 across Data Factory items) execution data:

docs/data-science/data-agent-creator-agent-overview.md

@@ -1,6 +1,6 @@
 ---
-title: creator agent for data agent in Microsoft Fabric (preview)
-description: Learn how to use creator agent to build and refine data agent configurations with AI-assisted setup, schema exploration, and few-shot generation.
+title: Build agent with AI mode for data agent in Microsoft Fabric (preview)
+description: Learn how to use the build agent with AI mode to configure and refine data agent configurations with AI-assisted setup, schema exploration, and few-shot generation.
 author: msft-shreyas
 ms.author: shradha
 ms.date: 06/26/2026
@@ -9,20 +9,20 @@ ms.search.form: data agent
 ai-usage: ai-assisted
 ---
 
-# Creator agent for data agent in Microsoft Fabric (preview)
+# Build agent with AI for data agent 
 
-Creator agent is a specialized AI assistant that helps you quickly build and refine the configurations that determine how a data agent behaves when answering questions over your data. Creator agent guides you through generating and improving the core configurations: **Agent Instructions**, **Data Source Instructions**, **Data Source Descriptions**, and **Example Queries**.
+**Build agent with AI** mode is a specialized AI assistant that helps you quickly build and refine the configurations that determine how a data agent behaves when answering questions over your data. **Build agent with AI** guides you through generating and improving the core configurations: **Agent Instructions**, **Data Source Instructions**, **Data Source Descriptions**, and **Example Queries**.
 
-Use creator agent in **Build agent with AI mode** to explore your connected data source, discuss the types of questions you want your data agent to answer, and review recommended configuration updates. When you're ready, accept the suggestions to apply them, then switch to **Test mode** to validate the improvements.
+Use the **Build agent with AI** mode to explore your connected data source, discuss the types of questions you want your data agent to answer, and review recommended configuration updates. When you're ready, accept the suggestions to apply them, then switch to **Test data agent** to validate the improvements.
 
-:::image type="content" source="media/data-agent-creator-agent-overview/creator-agent-entry-point.png" alt-text="Screenshot of the Build agent with AI option in the data agent ribbon that opens creator agent.":::
+:::image type="content" source="media/data-agent-creator-agent-overview/creator-agent-entry-point.png" alt-text="Screenshot of the Build agent with AI mode in the data agent ribbon that opens a creator assistant.":::
 
 > [!NOTE]
-> This feature is currently in preview and is available for **SQL and Eventhouse data sources only**. Creator agent doesn't work if you add any unsupported data sources to your data agent.
+> This feature is currently in preview and is available for **SQL and Eventhouse data sources only**. Build agent with AI mode doesn't work if you add any unsupported data sources to your data agent.
 
 ## Prerequisites
 
-Before using creator agent, make sure you have the following items:
+Before using **Build agent with AI**, make sure you have the following items:
 
 - A Fabric capacity (an F SKU or a trial capacity) that hosts your data agent.
 - The data agent tenant setting enabled by your Fabric administrator. For more information, see [Configure Fabric data agent tenant settings](data-agent-tenant-settings.md).
@@ -32,7 +32,7 @@ Before using creator agent, make sure you have the following items:
 
 ## Configuration types
 
-Creator agent helps you generate and manage four types of configuration artifacts that drive data agent quality:
+**Build agent with AI** helps you generate and manage four types of configuration artifacts that drive data agent quality:
 
 | Configuration | Description |
 |---|---|
@@ -43,28 +43,28 @@ Creator agent helps you generate and manage four types of configuration artifact
 
 ## Get started
 
-Follow these steps to start using creator agent with your data agent:
+Follow these steps to start using the **Build agent with AI** mode with your data agent:
 
 1. **Create a data agent** and add a supported data source with the relevant tables selected.
 
-1. **Open creator agent** by selecting **Build agent with AI** in the ribbon of your data agent.
+1. Select **Build agent with AI** in the ribbon of your data agent.
 
-   - **Build agent with AI** opens the creator agent chat experience.
+   - **Build agent with AI** opens the creator assistant chat experience.
    - **Test data agent** switches to the standard data agent test mode.
 
-1. **Start a conversation.** The creator agent opens with guidance on how to get started. Provide details about what you're trying to build, or ask a question you want the data agent to answer.
+1. **Start a conversation.** The build agent with AI mode opens with guidance on how to get started. Provide details about what you're trying to build, or ask a question you want the data agent to answer.
 
 ### Suggested first steps
 
-- Ask the creator agent a question you want the data agent to answer. 
-- Ask the creator agent to explore your schema and summarize key entities.
+- Ask the creator assistant a question you want the data agent to answer. 
+- Ask the creator assistant to explore your schema and summarize key entities.
 - Request Data Source Instructions and a few few-shot examples for two or three key questions.
 - Execute a query to validate the generated output.
 - Accept the updates and switch to Test mode to try sample questions.
 
 ## Key features
 
-The creator agent provides four core capabilities designed to help you discover your data, learn from existing patterns, and translate that context into better agent configurations.
+The Build agent with AI mode provides four core capabilities designed to help you discover your data, learn from existing patterns, and translate that context into better agent configurations.
 
 ### Schema exploration
 
@@ -76,7 +76,7 @@ Helps you quickly understand what's available in your attached data source by di
 - *"What columns should I use to join customers to orders?"*
 - *"Show me the key date fields and how they're used across tables."*
 
-The creator agent summarizes key entities, likely join paths, and important columns you can reference in Data Source Instructions or few-shot examples.
+The Build agent with AI mode summarizes key entities, likely join paths, and important columns you can reference in Data Source Instructions or few-shot examples.
 
 ### Query history exploration
 
@@ -88,10 +88,10 @@ Surfaces patterns from successfully executed queries, such as common joins, filt
 - *"Find common joins between factSales and dimensions and summarize the join keys."*
 - *"Are there standard filters we always apply for active customers?"*
 
-The creator agent summarizes recurring query structures and proposes candidate few-shot examples based on observed patterns.
+The build agent with AI mode summarizes recurring query structures and proposes candidate few-shot examples based on observed patterns.
 
 > [!NOTE]
-> Access to query history depends on your permissions in the data source. If query history isn't available, the creator agent falls back to schema-driven recommendations.
+> Access to query history depends on your permissions in the data source. If query history isn't available, the creator assistant falls back to schema-driven recommendations.
 
 ### Generate instructions and few-shot examples
 
@@ -104,7 +104,7 @@ Generates, improves, and helps you iterate on the configuration artifacts that d
 - *"Suggest five few-shots about revenue and returns."*
 - *"Please identify few-shots that contradict each other and recommend corrections."*
 
-The creator agent proposes updates and you choose whether to apply them. Review changes carefully before accepting, as updates **replace** your existing configuration blocks when applied during the current preview.
+The creator assistant proposes updates and you choose whether to apply them. Review changes carefully before accepting, as updates **replace** your existing configuration blocks when applied during the current preview.
 
 ### Execute query
 
@@ -116,7 +116,7 @@ Executes read-only queries against your attached data source to validate assumpt
 - *"Execute this query and tell me if the results look correct for active customers."*
 - *"Test the query you proposed for this few-shot and adjust it if it returns duplicates."*
 
-The creator agent shows the query it ran and summarizes the results. A successful run returns a result set you can review in the chat. If the query fails, the creator agent reports the error so you can adjust the instructions or few-shot examples.
+The creator assistant shows the query it ran and summarizes the results. A successful run returns a result set you can review in the chat. If the query fails, the creator assistant reports the error so you can adjust the instructions or few-shot examples.
 
 > [!IMPORTANT]
 > Only read-only queries are supported. Data modification operations are blocked.
@@ -135,15 +135,15 @@ Repeat this loop as needed to refine your data agent's behavior.
 
 ## Update configurations
 
-The creator agent proposes configuration changes through the chat experience. When a suggestion is ready:
+The build agent with AI mode proposes configuration changes through the chat experience. When a suggestion is ready:
 
 - Review the proposed changes in the chat.
 - Select **Accept** to apply the changes to your data agent, or decline to keep your current configuration.
 - Changes take effect immediately, enabling a tight **build → test → refine** loop.
 
 ## Unsupported configurations (preview)
 
-During the current preview, creator agent doesn't generate or manage the following configurations:
+**Build agent with AI** doesn't generate or manage the following configurations:
 
 - Data source selection (you must already attach a supported data source).
 - Schema selection.

docs/data-science/data-agent-toc/toc.yml

@@ -32,7 +32,7 @@ items:
         href: ../data-agent-routing.md
       - name: Add code interpreter
         href: ../data-agent-code-interpreter.md
-      - name: Configure a data agent with ai
+      - name: Configure a data agent with AI
         href: ../data-agent-creator-agent-overview.md
       - name: Fabric data agent runtime
         href: ../data-agent-runtime.md

docs/mirroring/snowflake-how-to-data-security.md

@@ -25,6 +25,17 @@ For more information, see Snowflake documentation on [Access Control Privileges
 > Any granular security established in the source Snowflake database must be re-configured in the mirrored database in Microsoft Fabric.
 > For more information, see [SQL granular permissions in Microsoft Fabric](../data-warehouse/sql-granular-permissions.md).
 
+## Supported authentication methods
+
+The following table lists which authentication methods are supported for mirroring for Snowflake:
+
+| Authentication method | Supported | Notes |
+|---|---|---|
+| Username and password | Yes | Snowflake native authentication |
+| Microsoft Entra ID (SSO) | Yes | Single sign-on via Entra ID |
+| Key pair authentication | Yes | RSA key pair for service account scenarios |
+| Workspace identity | No | Not currently supported for Snowflake |
+
 ## Data protection features
 
 You can secure column filters and predicate-based row filters on tables to roles and users in Microsoft Fabric: