Power BI

To set a verified answer, select a visual. Select the ... menu, and choose Set up a verified answer.

AI data schema:

AI instructions

Verified answers: Enables you to link a question users ask to a specific visual, allowing Copilot to produce a verified answer vetted by a human.

d deliver responses that are consistent, reliable, and aligned with your goals. This not only improves user trust but also accelerates adoption and impact across your organization.

After preparing your data for AI, you can test what your end-users will see through the Desktop Copilot report pane. The skill picker is a tool that gives you greater control over how Copilot responds by allowing you to select specific Copilot capabilities to enable. Currently, the skill picker includes three capabilities: Answer questions about the data: Uses Copilot to respond to questions based on a given semantic model Analyze report visuals: Enables Copilot to interpret and answer questions about the visuals within a report Create new report pages: Lets Copilot generate new report pages based on your prompts

Standalone Copilot experience (Home): Select Answer questions about the data Report Copilot Pane - Read Mode: Select Answer questions about the data and Analyze report visuals Report Copilot Pane - Edit Mode: Enable all three capabilities

Mark your model as prepped for AI

Considerations and limitations

Use Fabric User Data Functions with Pandas DataFrames and Series in Notebooks

Use Fabric User Data Functions with Pandas DataFrames and Series in Notebooks A major upgrade to Notebook integration with Fabric User Data Functions (UDFs) is now available: Pandas DataFrames and Series can now be used as input and output types—thanks to native integration with Apache Arrow! This update brings higher performance, improved efficiency, and greater scalability to your Fabric Notebooks—enabling seamless function reuse for large-scale data processing across Python, PySpark, Scala, and R. With this release, Pandas DataFrames and Series are now supported as first-class input and output types for UDFs, enabled by deep integration with Apache Arrow, a highly efficient columnar memory format optimized for analytics workloads.

Microsoft Fabric APIs Specification I’m excited to share that we’ve successfully published the Microsoft Fabric APIs Specification in the microsoft/fabric-rest-api-specs GitHub repository!

The Functions portal includes a Generate invocation code feature that allows for automatic generation of an Open API specification for Fabric User Data Functions.

OpenAPI spec generation in Fabric User Data Functions (Preview) The Functions portal includes a Generate invocation code feature that allows for automatic generation of an Open API specification for Fabric User Data Functions.

Expanded Data Agent Support for Large Data Sources (Preview) Data Agent is officially lifting restrictions on adding Data Sources with larger schema sizes. Users can now add Kusto, Semantic Model, Lakehouse, and Warehouse Data sources that contain over 100 Columns + Measures and more than 1000 Tables to the Data Agent. This change allows users to bring larger-scale databases and semantic models into Fabric’s Data Agent, unlocking deeper insights and enhanced capabilities.

Bulk delete query: This feature enables users to delete multiple saved queries at once, eliminating the need to remove them individually. It was introduced in response to user feedback highlighting the difficulty of managing large lists of saved queries without a multi-select delete option. In the query editor’s ‘Queries’ folders, users can now hold Shift, select multiple queries, and right-click to delete them all in a single action. This streamlines the cleanup process, making it easier to maintain an organized and clutter-free workspace with minimal effort.

Open your database in SQL Server Management Studio (SSMS): This feature integrates the Fabric SQL web-based editor with SSMS, allowing for a smooth transition to the desktop environment. With a single click from the query editor, SSMS launches and automatically fills in the connection details for your Fabric SQL database — no manual copy-paste or setup required. This streamlines the workflow for users who prefer or need the advanced capabilities and richer UI of SSMS, making it faster and easier to switch between tools while working with Fabric SQL.

Use Python Notebooks to Read/Write to Fabric SQL Databases (Preview) You can now read from and write to SQL databases in Microsoft Fabric using Python Notebooks, thanks to the new integration with the T-SQL magic command. This highly requested feature enables users to run powerful T-SQL queries directly within notebooks—combining scripting, visualizations, and explanatory text in one collaborative workspace. It supports rich, interactive charts, automated workflows, scheduled jobs, and secure sharing, making it easier than ever to analyze and operationalize SQL data seamlessly across the Fabric platform.

Microsoft Fabric APIs Specification

Notebook snapshot for running Notebooks

OpenAPI spec generation in Fabric User Data Functions (Preview)

Expanded Data Agent Support for Large Data Sources (Preview)

Use Python Notebooks to Read/Write to Fabric SQL Databases (Preview)

Optimizing Query Management: New Controls in the Editor

Create CI/CD-Enabled Dataflow Gen2 from Existing Dataflow Gen2 (Generally Available) Integrated Run History and Validation Feedback in Dataflow Gen2 Editor Improvements to SharePoint as a destination in Dataflow Gen2

New Category Filters Added to Template Gallery