Microsoft Power BI Data Analyst : PL-300

Exam Code: PL-300
Exam Name: Microsoft Power BI Data Analyst
Updated: Aug 06, 2026
Q & A: 515 Questions and Answers

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

Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

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

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Skills measured

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

Microsoft PL-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage and Maintain Workspace Items	10-15%	- Manage datasets 1. Manage gateways and data sources 2. Configure dataset metadata and properties 3. Manage dataset refresh scheduling 4. Resolve dataset permission issues - Create and manage workspaces 1. Endorse sensitive content with sensitivity labels 2. Publish and update a Power BI app 3. Configure workspace membership and roles 4. Configure content certification
Topic 2: Analyze the Data	10-15%	- Work with metrics in Power BI 1. Use the Decomposition Tree visual 2. Define and monitor KPIs 3. Track goals with scorecards 4. Create metrics using Microsoft measurements - Perform analysis in Power BI 1. Identify patterns and trends 2. Configure auto-insights 3. Generate Pinn visual insights 4. Use the Q&A visual 5. Apply Quick Insights
Topic 3: Prepare the Data	10-15%	- Get data from various data sources 1. Change data source settings 2. Configure dataset refresh 3. Choose between DirectQuery, Import, and Dual storage modes 4. Select a shared dataset or create a local copy 5. Use Microsoft Fabric and OneLake 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. Determine proper data types 4. Profile the data 5. Apply user-friendly naming conventions to columns and tables 6. Configure data loading and queries 7. Identify the data structure
Topic 4: Model the Data	25-30%	- Develop the data model 1. Create common date tables 2. Build hierarchies and hierarchical fields 3. Implement row-level security roles 4. Configure table relationships 5. Use the calculation group feature 6. Create calculated tables and columns - Optimize model performance 1. Optimize DirectQuery models 2. Optimize Dax queries 3. Validate performance and resolve issues 4. Improve user experience by hiding objects 5. Configure aggregations - Design a data model 1. Determine the appropriate granularity 2. Implement row-level security 3. Apply field metadata and groupings 4. Design a star schema or snowflake schema 5. Define tables and table schemas 6. Resolve performance issues with relationships - Create measures and DAX calculations 1. Create calculated measures 2. Create semi-additive measures 3. Use CALCULATE and FILTER functions 4. Use Time Intelligence functions 5. Implement context transition 6. Write and use basic DAX syntax
Topic 5: Visualize the Data	25-30%	- Build visuals 1. Apply sorting and filtering to visuals 2. Apply AI insights and enhanced analytics 3. Determine the appropriate visual type 4. Format and configure pages and report backgrounds 5. Configure data colors and shapes 6. Configure visual interactions and cross-filtering - Enhance reports for user experience 1. Design for mobile and specific layouts 2. Add accessibility features and compliance 3. Apply artistic formatting to visuals 4. Add tooltips and navigation - Enrich reports with advanced visuals 1. Create custom visuals 2. Use dynamic page navigation 3. Configure R and Python visuals 4. Integrate Microsoft Copilot for Power BI

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