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

PL-300 Korean real exams

Exam Code: PL-300-KR

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

Updated: Aug 18, 2026

Q & A: 472 Questions and Answers

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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.

  • 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.

  • 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.

  • 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.

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

What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam

The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.

Skills measured

  • Deploy and maintain assets (20-25%)
  • Model the data (30-35%)
  • Download the study guide in the preceding “Tip” box for more details about the skills measured on this exam.
  • Visualize and analyze the data (25-30%)
  • Prepare the data (15-20%)

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 secure Power BI15-20%- Manage workspaces and assets
  • 1. Create and configure workspaces
    • 2. Manage data refresh and scheduled updates
      • 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
              - 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
                    Visualize and analyze data25-30%- Perform data analysis
                    • 1. Interpret results and identify trends
                      • 2. Implement custom visuals and R/Python scripts
                        • 3. Create and manage bookmarks and tooltips
                          - Enhance reports with analytics
                          • 1. Use AI visuals, forecasts, and key influencers
                            • 2. Apply conditional formatting and custom visuals
                              • 3. Create paginated reports
                                - Create reports and dashboards
                                • 1. Create dashboards and pinned elements
                                  • 2. Choose appropriate visualizations
                                    • 3. Design report layout, formatting, and interactivity
                                      • 4. Apply filters, slicers, and drill-through
                                        Model the data25-30%- Optimize model performance
                                        • 1. Optimize relationships and calculations
                                          • 2. Reduce cardinality and data size
                                            • 3. Use aggregations and composite models
                                              - Design data models
                                              • 1. Create relationships: cardinality, cross-filter direction, security
                                                • 2. Implement role-playing dimensions
                                                  • 3. Define tables, columns, and hierarchies
                                                    • 4. Create common date tables
                                                      - Create model calculations
                                                      • 1. Implement aggregation and time intelligence
                                                        • 2. Use DAX: calculated columns, calculated tables, measures
                                                          • 3. Use CALCULATE and filter context
                                                            Prepare the data25-30%- Get or connect to data
                                                            • 1. Create and modify parameters
                                                              • 2. Identify and connect to data sources or shared semantic models
                                                                • 3. Choose storage modes: Import, DirectQuery, DirectLake
                                                                  • 4. Configure data source settings, credentials, and privacy levels
                                                                    - 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
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