Microsoft Power BI Data Analyst (PL-300 Korean Version) : PL-300 Korean valid dump torrent

  • Exam Code: PL-300-KR
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Korean Version)
  • Updated: Jul 25, 2026
  • Q&As: 515 Questions and Answers

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Passing the Microsoft PL-300 exam is a great way for individuals to demonstrate their expertise in using Power BI to analyze data and create valuable insights. It can also help individuals advance their careers in data analysis and business intelligence by demonstrating their competency to employers and clients. Overall, the Microsoft PL-300 exam is an excellent opportunity for individuals to demonstrate their proficiency in using Power BI and to enhance their career prospects in the field of data analysis.

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

Microsoft PL-300 Korean Exam Syllabus Topics:

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

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