Microsoft PL-300日本語 dumps - in .pdf

PL-300日本語 pdf
  • Exam Code: PL-300日本語
  • Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)
  • Updated: Sep 04, 2026
  • Q & A: 472 Questions and Answers
  • PDF Price: $69.99
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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: Sep 04, 2026
  • Q & A: 472 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $139.98  $89.99
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Microsoft PL-300日本語 dumps - Testing Engine

PL-300日本語 Testing Engine
  • Exam Code: PL-300日本語
  • Exam Name: Microsoft Power BI Data Analyst (PL-300日本語版)
  • Updated: Sep 04, 2026
  • Q & A: 472 Questions and Answers
  • Software Price: $69.99
  • Testing Engine

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

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PL-300日本語 exam dumps

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

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Microsoft PL-300日本語 Exam Syllabus Topics:

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

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