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

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

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:

  • 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.

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  • 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.

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

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Microsoft PL-300日本語 Exam Syllabus Topics:

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

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