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Skills measured

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

Exam PL-300: Microsoft Power BI Data Analyst

The Power BI data analyst delivers actionable insights by leveraging available data and applying domain expertise. The Power BI data analyst collaborates with key stakeholders across verticals to identify business requirements, cleans and transforms the data, and then designs and builds data models by using Power BI. The Power BI data analyst provides meaningful business value through easy-to-comprehend data visualizations, enables others to perform self-service analytics, and deploys and configures solutions for consumption. Candidates for this exam should be proficient using Power Query and writing expressions by using DAX.

Passing score: 700. Learn more about exam scores.

Part of the requirements for: Microsoft Certified: Power BI Data Analyst Associate

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

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Microsoft PL-300 Korean Exam Syllabus Topics:

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

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