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

PL-300 Korean real exams

Exam Code: PL-300-KR

Exam Name: Microsoft Power BI Data Analyst (PL-300 Korean Version)

Updated: Sep 02, 2026

Q & A: 472 Questions and Answers

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Microsoft PL-300 exam is designed to test a candidate's knowledge and skills in using Power BI to create data models, reports, and dashboards. PL-300-KR exam covers various topics such as data preparation, data modeling, data visualization, and data analysis. It also tests a candidate's ability to connect to various data sources, import data, and transform data using Power Query.

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Microsoft PL-300 exam is an essential certification for individuals who want to showcase their expertise in Power BI. Obtaining this certification can enhance one's career prospects and open up new opportunities in the field of data analytics and business intelligence. By passing PL-300-KR exam, candidates can prove their proficiency in using Power BI to transform data into actionable insights that drive business success.

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

Microsoft PL-300 Certification Exam, also known as the Microsoft Power BI Data Analyst exam, is designed to certify individuals who possess the skills and knowledge required to work with Power BI for data analysis and visualization. Microsoft Power BI Data Analyst (PL-300 Korean Version) certification is intended for data analysts, business analysts, and other professionals who work with data on a regular basis and want to develop their skills in Power BI. PL-300-KR exam covers a range of topics including data modeling, data preparation, data visualization, and report development.

Microsoft PL-300 Korean Exam Syllabus Topics:

SectionWeightObjectives
Manage and secure Power BI15-20%- Implement security and governance
  • 1. Manage permissions and access
    • 2. Apply data sensitivity labels and protection
      • 3. Configure row-level security (RLS) and object-level security
        - Manage workspaces and assets
        • 1. Manage data refresh and scheduled updates
          • 2. Create and configure workspaces
            • 3. Publish, update, and organize reports/datasets
              - Deploy and distribute content
              • 1. Embed content in applications or websites
                • 2. Monitor usage and performance
                  • 3. Share reports, dashboards, and apps
                    Model the data25-30%- Create model calculations
                    • 1. Use DAX: calculated columns, calculated tables, measures
                      • 2. Implement aggregation and time intelligence
                        • 3. Use CALCULATE and filter context
                          - Optimize model performance
                          • 1. Use aggregations and composite models
                            • 2. Reduce cardinality and data size
                              • 3. Optimize relationships and calculations
                                - Design data models
                                • 1. Define tables, columns, and hierarchies
                                  • 2. Create relationships: cardinality, cross-filter direction, security
                                    • 3. Create common date tables
                                      • 4. Implement role-playing dimensions
                                        Visualize and analyze data25-30%- Create reports and dashboards
                                        • 1. Design report layout, formatting, and interactivity
                                          • 2. Create dashboards and pinned elements
                                            • 3. Choose appropriate visualizations
                                              • 4. Apply filters, slicers, and drill-through
                                                - Enhance reports with analytics
                                                • 1. Apply conditional formatting and custom visuals
                                                  • 2. Use AI visuals, forecasts, and key influencers
                                                    • 3. Create paginated reports
                                                      - Perform data analysis
                                                      • 1. Create and manage bookmarks and tooltips
                                                        • 2. Interpret results and identify trends
                                                          • 3. Implement custom visuals and R/Python scripts
                                                            Prepare the data25-30%- Profile and clean data
                                                            • 1. Manage data types, columns, and rows
                                                              • 2. Evaluate data statistics and column properties
                                                                • 3. Transform data using Power Query Editor
                                                                  • 4. Resolve inconsistencies, null values, and errors
                                                                    - Get or connect to data
                                                                    • 1. Configure data source settings, credentials, and privacy levels
                                                                      • 2. Identify and connect to data sources or shared semantic models
                                                                        • 3. Create and modify parameters
                                                                          • 4. Choose storage modes: Import, DirectQuery, DirectLake
                                                                            - Load and configure data
                                                                            • 1. Configure data refresh settings
                                                                              • 2. Integrate data from multiple sources
                                                                                • 3. Define table structures and properties

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