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  • Exam Code: PL-300日本語
  • Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)
  • Updated: Aug 18, 2026
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About Microsoft Power BI Data Analyst (PL-300日本語版) : PL-300日本語 Exam braindumps

Passing Score, Duration & Questions For Microsoft PL-300 Exam

  • Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian

  • Number of Questions: 40-60 questions

  • The passing score: 700

  • Time Duration: 180 minutes

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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

Microsoft PL-300日本語 Exam Syllabus Topics:

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

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