Microsoft PL-300 Deutsch : 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 01, 2026
  • Q & A: 515 Questions and Answers

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About Microsoft PL-300 Deutsch Exam Questions

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What is the salary of the Microsoft Power BI Data Analyst Certified professional

The Average salary of different countries of Microsoft Power BI Data Analyst certified professionals:

  • India; INR 500,000

  • United States; USD 90,000

  • Canada; CAD 70,000

  • United Kingdom; POUNDS 60,000

  • Australia; AUD 75,000

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What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam

The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.

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%- Get or connect to data
  • 1. Choose storage modes: Import, DirectQuery, DirectLake
    • 2. Identify and connect to data sources or shared semantic models
      • 3. Configure data source settings, credentials, and privacy levels
        • 4. Create and modify parameters
          - Load and configure data
          • 1. Define table structures and properties
            • 2. Integrate data from multiple sources
              • 3. Configure data refresh settings
                - Profile and clean data
                • 1. Resolve inconsistencies, null values, and errors
                  • 2. Manage data types, columns, and rows
                    • 3. Transform data using Power Query Editor
                      • 4. Evaluate data statistics and column properties
                        Topic 2: Visualize and analyze data25-30%- Create reports and dashboards
                        • 1. Design report layout, formatting, and interactivity
                          • 2. Create dashboards and pinned elements
                            • 3. Apply filters, slicers, and drill-through
                              • 4. Choose appropriate visualizations
                                - Perform data analysis
                                • 1. Implement custom visuals and R/Python scripts
                                  • 2. Interpret results and identify trends
                                    • 3. Create and manage bookmarks and tooltips
                                      - Enhance reports with analytics
                                      • 1. Apply conditional formatting and custom visuals
                                        • 2. Create paginated reports
                                          • 3. Use AI visuals, forecasts, and key influencers
                                            Topic 3: Manage and secure Power BI15-20%- Implement security and governance
                                            • 1. Apply data sensitivity labels and protection
                                              • 2. Manage permissions and access
                                                • 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. Monitor usage and performance
                                                          • 2. Embed content in applications or websites
                                                            • 3. Share reports, dashboards, and apps
                                                              Topic 4: Model the data25-30%- 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. Implement role-playing dimensions
                                                                      • 2. Create common date tables
                                                                        • 3. Define tables, columns, and hierarchies
                                                                          • 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

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