Power BI Data Analyst with Fabric — Lesson 2: Data Preparation and Semantic Modeling

Lesson Overview

This lesson develops practical understanding of Data Preparation and Semantic Modeling within the Power BI Data Analyst with Fabric learning path.

Learners connect the key concepts to a realistic Power BI data analysis integrated with Microsoft Fabric scenario and document the decisions, evidence, risks, and improvement opportunities identified during the activity.

Learning Objectives

• Explain the purpose and role of data preparation and semantic modeling.
• Apply the lesson concepts to a practical Power BI data analysis integrated with Microsoft Fabric requirement.
• Recognize security, governance, operational, and quality considerations.
• Validate the result and recommend appropriate next steps.

Topics Covered

• dataflows and Power Query
• lakehouse or warehouse sources
• semantic models
• relationships and DAX

Hands-On Activity

1. Review the scenario, stakeholders, current process, requirements, and expected outcome.
2. Prepare the required Microsoft environment, design, query, report, script, or configuration.
3. Complete the guided implementation and record important decisions and dependencies.
4. Test the outcome using functional, security, data-quality, and operational checks.
5. Document issues, corrective actions, evidence, and an improvement recommendation.

Validation Checklist

• The solution or analysis matches the documented requirement.
• Configuration, calculations, queries, scripts, or workflows produce the expected result.
• Security, access, governance, data quality, and operational impacts are reviewed.
• Errors and limitations are documented with clear corrective actions.
• The final output includes reusable notes and practical next steps.

Lesson Outcome

After completing this lesson, learners can explain, apply, and validate data preparation and semantic modeling in a realistic workplace scenario.