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

PL-300 Deutsch real exams

Exam Code: PL-300-Deutsch

Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)

Updated: Sep 17, 2026

Q & A: 472 Questions and Answers

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Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

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

Learn more about the exam

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