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Updated: Aug 16, 2026

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Microsoft PL-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Power BI Data Analyst
Exam Number:PL-300
Available Languages:English, Japanese, Chinese (Simplified), Korean
Exam Format:Multiple choice, Case study, Drag and drop, Short answer
Certificate Validity Period:1 year
Exam Duration:120 minutes
Related Certifications:Microsoft Certified: Power BI Data Analyst Associate
Real Exam Qty:40-60
Exam Price:$165.00 USD
Passing Score:700 (scaled score, passing not publicly disclosed)
Sample Questions:Microsoft PL-300 Sample Questions
Exam Way:Online proctored exam (Pearson VUE) or in-person testing center
Pre Condition:No mandatory prerequisites, but Microsoft recommends having foundational knowledge of data concepts and Microsoft Power BI before taking the exam.
Official Syllabus URL:https://learn.microsoft.com/en-us/certifications/exams/pl-300/

Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

  • Model the data (30-35%)
  • Deploy and maintain assets (20-25%)
  • Visualize and analyze the data (25-30%)
  • Prepare the data (15-20%)

What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam

The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300

Microsoft PL-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Manage and Maintain Workspace Items10-15%- Create and manage workspaces
  • 1. Publish and update a Power BI app
  • 2. Configure content certification
  • 3. Endorse sensitive content with sensitivity labels
  • 4. Configure workspace membership and roles
- Manage datasets
  • 1. Manage gateways and data sources
  • 2. Configure dataset metadata and properties
  • 3. Resolve dataset permission issues
  • 4. Manage dataset refresh scheduling
Topic 2: Model the Data25-30%- Create measures and DAX calculations
  • 1. Use CALCULATE and FILTER functions
  • 2. Write and use basic DAX syntax
  • 3. Implement context transition
  • 4. Create calculated measures
  • 5. Use Time Intelligence functions
  • 6. Create semi-additive measures
- Design a data model
  • 1. Define tables and table schemas
  • 2. Apply field metadata and groupings
  • 3. Determine the appropriate granularity
  • 4. Design a star schema or snowflake schema
  • 5. Implement row-level security
  • 6. Resolve performance issues with relationships
- Develop the data model
  • 1. Create common date tables
  • 2. Implement row-level security roles
  • 3. Use the calculation group feature
  • 4. Create calculated tables and columns
  • 5. Build hierarchies and hierarchical fields
  • 6. Configure table relationships
- Optimize model performance
  • 1. Optimize Dax queries
  • 2. Improve user experience by hiding objects
  • 3. Validate performance and resolve issues
  • 4. Optimize DirectQuery models
  • 5. Configure aggregations
Topic 3: Analyze the Data10-15%- Perform analysis in Power BI
  • 1. Use the Q&A visual
  • 2. Generate Pinn visual insights
  • 3. Configure auto-insights
  • 4. Identify patterns and trends
  • 5. Apply Quick Insights
- Work with metrics in Power BI
  • 1. Use the Decomposition Tree visual
  • 2. Track goals with scorecards
  • 3. Define and monitor KPIs
  • 4. Create metrics using Microsoft measurements
Topic 4: Visualize the Data25-30%- Build visuals
  • 1. Apply AI insights and enhanced analytics
  • 2. Configure data colors and shapes
  • 3. Configure visual interactions and cross-filtering
  • 4. Determine the appropriate visual type
  • 5. Format and configure pages and report backgrounds
  • 6. Apply sorting and filtering to visuals
- Enhance reports for user experience
  • 1. Add accessibility features and compliance
  • 2. Apply artistic formatting to visuals
  • 3. Add tooltips and navigation
  • 4. Design for mobile and specific layouts
- Enrich reports with advanced visuals
  • 1. Integrate Microsoft Copilot for Power BI
  • 2. Create custom visuals
  • 3. Configure R and Python visuals
  • 4. Use dynamic page navigation
Topic 5: Prepare the Data10-15%- Get data from various data sources
  • 1. Use Microsoft Fabric and OneLake
  • 2. Choose between DirectQuery, Import, and Dual storage modes
  • 3. Select a shared dataset or create a local copy
  • 4. Configure dataset refresh
  • 5. Identify and connect to a data source
  • 6. Change data source settings
- Clean, transform, and load the data
  • 1. Configure data loading and queries
  • 2. Profile the data
  • 3. Resolve data quality issues
  • 4. Identify the data structure
  • 5. Apply user-friendly naming conventions to columns and tables
  • 6. Determine proper data types
  • 7. Design and build intermediate tables

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