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  • Exam Code: PL-300 Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

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

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