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Microsoft PL-300日本語 Valid Braindumps - Microsoft Power BI Data Analyst (PL-300日本語版)

PL-300日本語
  • Exam Code: PL-300J
  • Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)
  • Updated: Aug 18, 2026
  • Q & A: 472 Questions and Answers
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Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

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

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

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Skills measured

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

Microsoft PL-300日本語 Exam Syllabus Topics:

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

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