Microsoft PL-300 Deutsch real exam prep : Microsoft Power BI Data Analyst (PL-300 Deutsch Version)

  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Aug 23, 2026
  • Q&As: 472 Questions and Answers

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PL-300 Deutsch exam dumps

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Microsoft Power BI Data Analyst Certification Exam Reference

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

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
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
        - Profile and clean data
        • 1. Evaluate data statistics and column properties
          • 2. Resolve inconsistencies, null values, and errors
            • 3. Transform data using Power Query Editor
              • 4. Manage data types, columns, and rows
                - Get or connect to data
                • 1. Configure data source settings, credentials, and privacy levels
                  • 2. Create and modify parameters
                    • 3. Identify and connect to data sources or shared semantic models
                      • 4. Choose storage modes: Import, DirectQuery, DirectLake
                        Manage and secure Power BI15-20%- 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
                                    - 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
                                          Visualize and analyze data25-30%- Perform data analysis
                                          • 1. Interpret results and identify trends
                                            • 2. Create and manage bookmarks and tooltips
                                              • 3. Implement custom visuals and R/Python scripts
                                                - Enhance reports with analytics
                                                • 1. Create paginated reports
                                                  • 2. Use AI visuals, forecasts, and key influencers
                                                    • 3. Apply conditional formatting and custom visuals
                                                      - 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
                                                              Model the data25-30%- Design data models
                                                              • 1. Define tables, columns, and hierarchies
                                                                • 2. Create relationships: cardinality, cross-filter direction, security
                                                                  • 3. Implement role-playing dimensions
                                                                    • 4. Create common date tables
                                                                      - Optimize model performance
                                                                      • 1. Optimize relationships and calculations
                                                                        • 2. Reduce cardinality and data size
                                                                          • 3. Use aggregations and composite models
                                                                            - Create model calculations
                                                                            • 1. Use DAX: calculated columns, calculated tables, measures
                                                                              • 2. Use CALCULATE and filter context
                                                                                • 3. Implement aggregation and time intelligence

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