Microsoft Power Apps with AI Builder — Lesson 1: AI Builder Use Cases and Data

Learning Objectives

• Identify document, prediction, classification, object, and prebuilt AI use cases
• Assess value, feasibility, risk, data quality, privacy, and human oversight
• Prepare, label, secure, and validate training and test data

Detailed Topics

• Identify document: Identify document, prediction, classification, object, and prebuilt AI use cases
• Assess value: Assess value, feasibility, risk, data quality, privacy, and human oversight
• Prepare: Prepare, label, secure, and validate training and test data

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, application, automation, analysis, or administration 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

Create an AI Builder use-case and data-readiness plan.

• 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 AI Builder Use Cases and Data in a realistic scenario and validate the result with documented evidence.