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Last Updated: Sep 10, 2026

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

Microsoft PL-300 certification exam is intended for individuals who have a strong background in data analysis and are familiar with the concepts of data modeling, data visualization, and data manipulation. PL-300-Deutsch exam evaluates the knowledge of candidates in designing and building Power BI solutions, creating reports and dashboards, and managing the Power BI service.

Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
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. 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. Monitor usage and performance
                • 2. Share reports, dashboards, and apps
                  • 3. Embed content in applications or websites
                    Visualize and analyze data25-30%- Perform data analysis
                    • 1. Create and manage bookmarks and tooltips
                      • 2. Implement custom visuals and R/Python scripts
                        • 3. Interpret results and identify trends
                          - Enhance reports with analytics
                          • 1. Use AI visuals, forecasts, and key influencers
                            • 2. Create paginated reports
                              • 3. Apply conditional formatting and custom visuals
                                - 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
                                        Model the data25-30%- Design data models
                                        • 1. Implement role-playing dimensions
                                          • 2. Create relationships: cardinality, cross-filter direction, security
                                            • 3. Create common date tables
                                              • 4. Define tables, columns, and hierarchies
                                                - 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 DAX: calculated columns, calculated tables, measures
                                                        • 2. Implement aggregation and time intelligence
                                                          • 3. Use CALCULATE and filter context
                                                            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
                                                                  - Profile and clean data
                                                                  • 1. Manage data types, columns, and rows
                                                                    • 2. Evaluate data statistics and column properties
                                                                      • 3. Transform data using Power Query Editor
                                                                        • 4. Resolve inconsistencies, null values, and errors
                                                                          - Get or connect to data
                                                                          • 1. Identify and connect to data sources or shared semantic models
                                                                            • 2. Create and modify parameters
                                                                              • 3. Choose storage modes: Import, DirectQuery, DirectLake
                                                                                • 4. Configure data source settings, credentials, and privacy levels
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