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Microsoft PL-300 Korean

PL-300 Korean

Exam Code: PL-300-KR

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

Updated: Aug 26, 2026

Q&A Number: 472 Q&As

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About Microsoft PL-300 Korean Exam Questions and Answers

Passing Score, Duration & Questions For Microsoft PL-300 Exam

  • Time Duration: 180 minutes

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

  • Number of Questions: 40-60 questions

  • The passing score: 700

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PL-300 Korean Online Test Engine

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What are the steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam

The steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam are as follows:

  • Then you need to select your preferred payment method which includes credit card or PayPal account etc., and you will receive an email confirmation after successful payment using your credit card or PayPal account etc.

  • Go to the official website of Microsoft, and click on “Microsoft Learning”.

  • You will see a list of courses that are available on this website. Select “Microsoft Power BI Data Analyst Certification Exam” and click on “Enroll Now” button.

  • After successful registration process, you will receive an email confirmation from Microsoft with all the details about your course including access code etc., which will be sent to your registered email ID provided during registration process.

  • The next step is to fill out your personal information such as name, email address, country and state city etc., which is mandatory to complete your registration process successfully.

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

Microsoft PL-300 Korean Exam Syllabus Topics:

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

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