If you will be delivering this session, check the session-delivery-sources folder for slides, scripts, and other resources.

This lab explores how organizations can leverage their existing data by unifying their data estate using Microsoft Fabric. Go from data chaos to clarity in this hands-on lab. Learn data ingestion, transformation, and AI-driven insights using Microsoft Fabric and Azure Databricks. Build scalable data solutions and explore agentic AI for smarter decisions and real business impact

By the end of this session, learners will be able to:

• Set up an environment
• Ingest raw data using ADLS Gen2 shortcuts
• Transform and analyze data using Azure Databricks
• Mirror Azure Databricks data in Microsoft Fabric
• Build data pipelines using Delta Live Tables
• Create Power BI reports using the semantic model with shortcut data
• Create Data Agents and Foundry agents for AI insights

1. Microsoft Fabric (including OneLake, Power BI, Copilot, Data Agents)
2. Azure Databricks (catalog mirroring with Fabric)
3. Power BI (AI capabilities)
4. Microsoft Foundry

Resources	Links	Description
AI Tour 2026 Resource Center	https://aka.ms/AITour26-Resource-Center	Links to all repos for AI Tour 26 Sessions
Microsoft Foundry Community Discord		Connect with the Microsoft Foundry Community!
Learn at AI Tour	https://aka.ms/LearnAtAITour	Continue learning on Microsoft Learn

Additional languages are coming soon.

Someleze Diko 📢

Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.

The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Microsoft Foundry portal, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.

Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using Performance and Quality and Risk and Safety evaluators. You also have the ability to create and evaluate with custom evaluators.

You can evaluate your AI application in your development environment using the Azure AI Evaluation SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Foundry portal .