Try and practice the latest Microsoft : PL-300 real questions & answers

Last Updated: Aug 06, 2026

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Skills measured

  • Prepare the data (15-20%)
  • Model the data (30-35%)
  • Download the study guide in the preceding “Tip” box for more details about the skills measured on this exam.
  • Deploy and maintain assets (20-25%)
  • Visualize and analyze the data (25-30%)

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The Microsoft Power BI Data Analyst Certification Exam is a certification exam that will help you to demonstrate your expertise in using the Microsoft Power BI platform. This exam tests the candidate's ability to analyze data with Power BI and create reports that are accurate, insightful and easy to read. Once you pass this exam, you will receive a certificate that shows your competence in performing these tasks. The certificate is valid for three years, so if you want to keep your skills up-to-date then you should take additional training during this time period.

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

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Microsoft PL-300 Exam Syllabus Topics:

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

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