PL-7004: Implement AI Models with Microsoft Power Platform AI Builder — Lesson 2: Model Creation and Evaluation

Learning Objectives

• Create, train, test, publish, and version document, prediction, or recognition models
• Interpret confidence, errors, weak classes, precision, recall, and data gaps
• Define acceptance criteria and low-confidence handling

Detailed Topics

• Create: Create, train, test, publish, and version document, prediction, or recognition models
• Interpret confidence: Interpret confidence, errors, weak classes, precision, recall, and data gaps
• Define acceptance criteria and low-confidence handling: Define acceptance criteria and low-confidence handling

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, application, automation, analysis, security, or development 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 evaluate an AI Builder model.

• 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 Model Creation and Evaluation in a realistic scenario and validate the result with documented evidence.