Power BI Advanced with Embedding — Lesson 1: Advanced Modeling and Analytical Design

Learning Objectives

• Design enterprise-grade semantic models and reusable analytical layers
• Implement advanced DAX patterns, calculation groups, and business logic
• Plan incremental refresh, aggregations, and model lifecycle controls

Detailed Topics

• Design enterprise-grade semantic models and reusable analytical layers: Design enterprise-grade semantic models and reusable analytical layers
• Implement advanced DAX patterns: Implement advanced DAX patterns, calculation groups, and business logic
• Plan incremental refresh: Plan incremental refresh, aggregations, and model lifecycle controls

Guided Demonstration

1. Review the requirement, environment, dependencies, roles, risks, and expected outcome.
2. Demonstrate the relevant configuration, analysis, code, or administration workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, or test evidence.
4. Compare the observed result with the stated acceptance criteria.

Hands-On Activity

Design an advanced semantic model for an enterprise reporting use case.

• Document assumptions and the planned sequence before making changes.
• Complete the activity and preserve evidence of normal and exception behavior.
• Record issues, corrective actions, and reusable operational or development notes.

Troubleshooting Focus

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

Validation Checklist

• The requirement and expected outcome are clear.
• The selected configuration, design, query, code, or process is documented.
• Normal, boundary, failure, security, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Advanced Modeling and Analytical Design in a realistic scenario and validate the result with documented evidence.