Smart Agents for Microsoft Fabric – Copilot and AI Skill — Lesson 2: Data Grounding and AI Skill Configuration

Learning Objectives

• Prepare governed data, metadata, relationships, instructions, examples, and retrieval context
• Configure AI skills and validate grounding, relevance, permissions, lineage, and response evidence
• Validate data grounding and ai skill configuration decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Prepare governed data: Prepare governed data, metadata, relationships, instructions, examples, and retrieval context
• Configure AI skills and validate grounding: Configure AI skills and validate grounding, relevance, permissions, lineage, and response evidence
• Validate data grounding and ai skill configuration decisions with documented requirements: Validate data grounding and ai skill configuration decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, creation, development, administration, analysis, or support workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Build and test a grounded Fabric AI skill.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Data Grounding and AI Skill Configuration in a realistic scenario and validate the result with documented evidence.