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Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

  • Deploy and maintain assets (20-25%)
  • Model the data (30-35%)
  • Visualize and analyze the data (25-30%)
  • Prepare the data (15-20%)

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Microsoft PL-300 is an exam that focuses on the skills necessary to become a Microsoft Power BI Data Analyst. Microsoft Power BI Data Analyst (PL-300日本語版) certification exam is designed to test the abilities of data analysts in designing and developing Power BI solutions. PL-300J exam is intended for individuals who have experience in data analysis, data visualization, and business intelligence.

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Microsoft Power BI is a powerful business intelligence tool that allows users to analyze and visualize data in a variety of ways. As businesses increasingly rely on data to make informed decisions, the demand for professionals who are skilled in Power BI has grown tremendously. To validate the skills and expertise of these professionals, Microsoft has introduced the PL-300 certification exam for Power BI Data Analysts.

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

Microsoft PL-300日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
        - Deploy and distribute content
        • 1. Share reports, dashboards, and apps
          • 2. Embed content in applications or websites
            • 3. Monitor usage and performance
              - Manage workspaces and assets
              • 1. Manage data refresh and scheduled updates
                • 2. Create and configure workspaces
                  • 3. Publish, update, and organize reports/datasets
                    Topic 2: 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
                          - 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. Choose storage modes: Import, DirectQuery, DirectLake
                                • 4. Create and modify parameters
                                  - Profile and clean data
                                  • 1. Transform data using Power Query Editor
                                    • 2. Resolve inconsistencies, null values, and errors
                                      • 3. Manage data types, columns, and rows
                                        • 4. Evaluate data statistics and column properties
                                          Topic 3: Model the data25-30%- Optimize model performance
                                          • 1. Optimize relationships and calculations
                                            • 2. Use aggregations and composite models
                                              • 3. Reduce cardinality and data size
                                                - Create model calculations
                                                • 1. Use DAX: calculated columns, calculated tables, measures
                                                  • 2. Implement aggregation and time intelligence
                                                    • 3. Use CALCULATE and filter context
                                                      - Design data models
                                                      • 1. Create relationships: cardinality, cross-filter direction, security
                                                        • 2. Define tables, columns, and hierarchies
                                                          • 3. Implement role-playing dimensions
                                                            • 4. Create common date tables
                                                              Topic 4: Visualize and analyze data25-30%- Perform data analysis
                                                              • 1. Create and manage bookmarks and tooltips
                                                                • 2. Interpret results and identify trends
                                                                  • 3. Implement custom visuals and R/Python scripts
                                                                    - Create reports and dashboards
                                                                    • 1. Create dashboards and pinned elements
                                                                      • 2. Design report layout, formatting, and interactivity
                                                                        • 3. Choose appropriate visualizations
                                                                          • 4. Apply filters, slicers, and drill-through
                                                                            - Enhance reports with analytics
                                                                            • 1. Use AI visuals, forecasts, and key influencers
                                                                              • 2. Apply conditional formatting and custom visuals
                                                                                • 3. Create paginated reports

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