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

Retirement date: none

This exam measures your ability to accomplish the following technical tasks: prepare the data; model the data; visualize and analyze the data; and deploy and maintain assets.

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

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Microsoft PL-300 Korean Exam Syllabus Topics:

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

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