Understanding Microsoft Fabric: AI Integration and Copilot Features That Change How You Work with Data
Microsoft Fabric is no longer just a data platform. With Copilot embedded across every workload, AI functions built into the Warehouse, data agents that answer questions in plain English, and Fabric IQ turning your data into an intelligence layer, Fabric is evolving into a platform where AI is not an add-on but a core part of how you build, analyze, and act on data.
In this article, we will cover every AI capability in Microsoft Fabric: Copilot for notebooks, Power BI, and Data Warehouse, built-in AI functions for text analytics in T-SQL, data agents for conversational analytics, and Fabric IQ as the unified intelligence layer. You will learn what is GA, what Preview is, what it costs, and how to get started.
If you are preparing for the DP-700: Fabric Data Engineer Associate exam, AI features are increasingly tested. Microsoft expects you to know where Copilot fits in the data engineering workflow and how to use AI capabilities to accelerate development.
This article is part of the Understanding Microsoft Fabric series, a practical guide designed to help you master every key component of Fabric and prepare for the DP-700 certification.
If you have been following the series, we have already covered:
Understanding Microsoft Fabric: Cost and Performance Optimization
Understanding Microsoft Fabric: Monitoring and Troubleshooting
Understanding Microsoft Fabric: Shortcuts and External Connections
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1. Copilot in Microsoft Fabric: Overview and Architecture
Copilot in Microsoft Fabric is a generative AI assistant powered by Azure OpenAI (GPT-4 architecture) that is embedded across every Fabric workload. It understands the context of your workspace, your data, and your current task, and generates code, insights, and recommendations based on natural language prompts.
How It Works
Copilot uses large language models (LLMs) to interpret your prompts and translate them into actions within Fabric. When you ask Copilot to write a Spark transformation or generate a DAX measure, it considers your current context, including the attached Lakehouse, workspace tables, loaded DataFrames, and semantic model structure.
Status and Availability
Copilot is enabled by default for all Fabric tenants. Administrators can disable it in the Fabric Admin Portal if the organization is not ready. Key status details:
Copilot for Power BI (report pane): Generally available.
Copilot for Power BI (standalone, full-screen experience): Preview. Enabled by default since September 2025.
Copilot for Power BI Apps: Preview.
Copilot for Data Engineering and Data Science (notebooks): Preview.
Copilot for Data Factory, Data Warehouse, and Real-Time Intelligence: Preview.
Requirements
A paid Fabric capacity (F2 or higher) or Power BI Premium capacity (P1 or higher). Trial SKUs and trial capacities do not support Copilot.
A Power BI Pro or Premium Per User (PPU) license alone is not sufficient. Copilot requires organizational capacity.
Your Fabric capacity must be in a supported region. If outside the US or France, the tenant admin must enable the cross-geo data processing setting.
Sovereign clouds (Azure Government, Azure China) have limited or delayed availability.
Cost
Copilot usage consumes CUs from your Fabric capacity. There is no additional per-query charge, but heavy Copilot usage will contribute to overall capacity utilization. As of February 2026, organizations can designate specific capacities as “Fabric Copilot capacities” to consolidate and isolate Copilot consumption.
DP-700 Exam Tip
Copilot requires paid Fabric capacity (F2+), not just a Pro or PPU license. If a question describes Copilot not being available, check for capacity requirements and region support.
2. Copilot for Data Engineering: AI-Powered Notebooks
For data engineers, Copilot in Fabric notebooks is where AI makes the biggest day-to-day impact. It acts as a coding assistant that understands your data environment.
What Copilot Can Do in Notebooks
Generate code from natural language. Describe what you want (“Read the sales table from my Lakehouse, filter for 2024, and calculate total revenue by region”) and Copilot generates the PySpark or SparkSQL code.
Explain existing code. Select a cell and ask Copilot to explain what it does. Useful when working with unfamiliar notebooks or inheriting someone else’s code.
Fix and optimize code. Copilot can analyze errors, suggest fixes, and recommend performance improvements like reducing shuffles or applying better join strategies.
Diagnose notebook failures. When a notebook fails, Copilot can analyze the error stack trace and suggest remediation steps.
Context-aware suggestions. Copilot automatically understands your attached Lakehouses, workspace tables, loaded DataFrames, and Spark configurations. You do not need to describe your environment.
How It Integrates
Copilot works in two complementary ways in notebooks:
Chat pane: A side panel for multi-step workflows, pipeline building, dataset exploration, and reviewing generated code with diff view. Supports notebook-wide code generation and refactoring across cells.
Inline code suggestions: Contextual autocomplete as you type, similar to GitHub Copilot. Suggests code completions based on the current cell and notebook context.
Limitations to Know
Copilot is currently optimized for Lakehouse scenarios. If you work with other sources like SQL databases, specify the data source explicitly in your prompts.
Private link configurations may prevent the chat pane from loading. Inline suggestions may still work.
Code generation with recently released or fast-moving libraries may include inaccuracies. Always review Copilot suggestions before running them.
DP-700 Exam Tip
If a question describes a data engineer needing to quickly generate Spark code for data transformation, Copilot in notebooks is the answer. Remember that it is context-aware and does not require you to describe your Lakehouse structure.
3. Copilot for Power BI: From Prompts to Reports
Copilot in Power BI transforms how reports are created and consumed.
Key Capabilities
Generate reports from prompts. Describe the report you want (“Create a sales dashboard showing revenue by region and product category with monthly trends”) and Copilot generates the visuals.
Generate DAX measures. Describe the calculation in plain English and Copilot writes the DAX formula.
Summarize report pages. Copilot generates narrative summaries of what the data shows, highlighting trends, outliers, and key metrics.
Generate synonyms for Q&A. Copilot creates alternative names for columns and measures, improving the Q&A natural language experience.
Standalone experience (Preview). Unlike the report-scoped pane, the standalone Copilot can find and answer questions about any report, semantic model, or data agent you have access to across your entire workspace.
App-scoped experience (Preview). For Power BI apps, Copilot operates across the curated content included in the app, helping users discover and ask questions about app reports and dashboards.
Prompt Length
As of February 2026, the Copilot input character limit has increased from 500 to 10,000 characters across all surfaces (standalone, report pane, apps, mobile, and embed).
DP-700 Exam Tip
If a question asks how a business user can get a natural language summary of a Power BI report, the answer is Copilot for Power BI (narrative summary). If it asks how to auto-generate DAX, the answer is Copilot for Power BI (DAX generation).
4. AI Functions in the Data Warehouse: Text Analytics in T-SQL
As of March 2026, Fabric Data Warehouse introduces built-in AI functions that bring text analytics directly into T-SQL. This eliminates the need for external AI services or complex Python pipelines for common text processing tasks.
What You Can Do
Text extraction: Pull structured data from unstructured text fields (addresses, dates, entities from free-form notes).
Sentiment analysis: Score text on a negative-to-positive scale (customer feedback, support tickets, social media mentions).
Text classification: Categorize content like application logs, incident reports, or customer inquiries based on contextual understanding.
Translation and summarization: Process multilingual text or condense long documents.
Why This Matters
Previously, processing unstructured text required building Spark notebooks, calling Azure Cognitive Services APIs, or running Python scripts. With AI functions in T-SQL, data engineers and analysts can keep text processing inside the Warehouse using familiar SQL patterns. This simplifies architecture and reduces the number of moving parts in your data pipeline.
Multimodal AI in Notebooks
Beyond the Warehouse, Fabric notebooks also support multimodal AI functions that work with PDFs, images, and text files. With just a few lines of code, you can perform summarization, classification, sentiment analysis, and more on unstructured data directly in your notebook workflow.
DP-700 Exam Tip
If a question describes a need to analyze sentiment in customer feedback stored in a Warehouse table, the answer is Warehouse AI functions in T-SQL, not a separate Spark notebook or external service.
5. Data Agents: Conversational Analytics Over Your Fabric Data
Data agents are one of the most impactful AI features in Fabric. They are generally available as of FabCon 2026 and allow you to build conversational Q&A systems that answer questions about your data in plain English.
How Data Agents Work
A data agent connects to your Fabric data sources (up to five at a time) and uses generative AI (Azure OpenAI Assistant APIs) to process questions, determine the most relevant data source, generate queries, and return structured answers.
When a user asks “What were our top 10 products by revenue last quarter?”, the data agent:
Identifies the relevant data source (Lakehouse, Warehouse, or semantic model).
Generates the appropriate query (SQL for relational sources, DAX for semantic models, KQL for Eventhouse).
Executes the query using the user’s permissions.
Returns a human-readable answer.
Supported Data Sources
Data agents support connections to Lakehouses, Warehouses, Power BI semantic models, Eventhouse (KQL databases), SQL databases, mirrored databases, ontologies, and Microsoft Graph.
Security and Governance
Data agents respect Fabric permissions. When a user asks a question, the agent uses that user’s identity to query the data source, meaning RLS, CLS, and item permissions are enforced. The agent only returns data the user has permission to see.
Microsoft Purview DLP policies can detect and restrict access to sensitive data in assets that the agent queries. Audit, eDiscovery, and retention policies apply to agent interactions.
Configuration
Setting up a data agent is similar to building a Power BI report:
Create a Data Agent item in your workspace.
Select up to five data sources.
Provide agent-level instructions and data-source-specific instructions.
Add example queries to guide the agent’s behavior.
Publish and share with users.
DP-700 Exam Tip
Data agents are GA. If a question describes a scenario where non-technical users need to ask questions about Fabric data in natural language, the answer is a Fabric data agent. Remember: they support up to 5 data sources and respect user-level security.
6. Fabric IQ: The Unified Intelligence Layer (Preview)
Fabric IQ is the most ambitious AI addition to Fabric. Announced at Microsoft Ignite in November 2025, it transforms Fabric from a unified data platform into a unified intelligence platform.
What Fabric IQ Does
Fabric IQ provides a layer of semantic understanding on top of your data. Instead of each team having its own definitions of “Customer,” “Revenue,” or “Active User,” Fabric IQ creates a single, consistent business model that all tools, including Power BI, notebooks, and agents, use to interpret data.
Items in Fabric IQ
Fabric IQ is a workload that includes several items:
Ontology (Preview): A model of your business that captures entities (Customer, Order, Asset), relationships (Order belongs to Customer), attributes, constraints, and actions. Ontologies can be generated from existing Power BI semantic models.
Graph (Preview): Visual representation and native graph storage for exploring relationships between business entities. Supports path finding, dependency analysis, and graph algorithms.
Data Agent (GA): Conversational Q&A systems that query your data using natural language (covered in section 5).
Operations Agent (Preview): AI agents that monitor real-time data and recommend business actions. Unlike data agents (which answer questions), operations agents proactively observe data and act.
Plan (Preview): A unified planning capability for budgets, forecasts, and scenario analysis directly within Fabric.
Power BI Semantic Model: Used within Fabric IQ to ensure consistent business language across reporting and AI.
The IQ Ecosystem
Fabric IQ is part of a broader intelligence layer called Microsoft IQ, which combines three sources of context:
Fabric IQ: Live business data from OneLake.
Foundry IQ: Knowledge graphs across documents, emails, and content.
Work IQ: Knowledge from Microsoft 365 (documents, emails, chats).
Together, these enable AI agents that understand not just your data but your business context, institutional knowledge, and organizational workflows.
DP-700 Exam Tip
Fabric IQ is in Preview. If a question asks about creating a unified business model across Fabric that drives consistent definitions for AI agents, the answer is Fabric IQ with Ontology. Know the difference between data agents (answer questions) and operations agents (monitor and act).
Common Mistakes to Avoid When Using AI in Microsoft Fabric
Blindly trusting Copilot-generated code. Copilot is an assistant, not an oracle. Always review generated code, especially for data transformations that affect production pipelines. AI-generated content can include inaccuracies or fabrications.
Using Copilot without understanding the cost. Copilot consumes CUs from your Fabric capacity. Heavy usage during peak hours can contribute to throttling. Consider using a dedicated Copilot capacity to isolate AI consumption.
Expecting Copilot to work without proper semantic models. Copilot for Power BI is only as good as your semantic model. Friendly column names, clear relationships, and well-defined measures dramatically improve Copilot accuracy. Invest in your semantic model before relying on Copilot.
Confusing data agents with Copilot. Copilot helps YOU build things (code, reports, measures). Data agents help END USERS ask questions about data. They serve different audiences and purposes.
Not enabling Copilot at the right level. Copilot must be enabled at the tenant level to use the standalone experience. Enabling it only at the capacity level is not sufficient for all features.
DP-700 Exam Tip
The exam may test the distinction between Copilot (developer assistant) and data agents (end-user Q&A). Also know that Copilot accuracy depends on semantic model quality and that all AI features consume CUs.
What is Next
In the next article, we will explore Workspace Management in Microsoft Fabric, covering workspace settings, capacity assignment, domain organization, Git integration, and how to structure workspaces for development, testing, and production environments.
Make sure to bookmark this series so you do not miss any upcoming articles.