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: Oct 06, 2026
  • Q & A: 472 Questions and Answers

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About Microsoft PL-300 Deutsch Exam

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:

  • United States; USD 90,000

  • United Kingdom; POUNDS 60,000

  • Australia; AUD 75,000

  • Canada; CAD 70,000

  • India; INR 500,000

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

Passing Score, Duration & Questions For Microsoft PL-300 Exam

  • Time Duration: 180 minutes

  • Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian

  • The passing score: 700

  • Number of Questions: 40-60 questions

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Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian

Retirement date: none

This exam measures your ability to accomplish the following technical tasks: prepare the data; model the data; visualize and analyze the data; and deploy and maintain assets.

Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Prepare the data25-30%- Load and configure data
  • 1. Define table structures and properties
    • 2. Configure data refresh settings
      • 3. Integrate data from multiple sources
        - Profile and clean data
        • 1. Transform data using Power Query Editor
          • 2. Resolve inconsistencies, null values, and errors
            • 3. Manage data types, columns, and rows
              • 4. Evaluate data statistics and column properties
                - Get or connect to data
                • 1. Create and modify parameters
                  • 2. Configure data source settings, credentials, and privacy levels
                    • 3. Identify and connect to data sources or shared semantic models
                      • 4. Choose storage modes: Import, DirectQuery, DirectLake
                        Manage and secure Power BI15-20%- Implement security and governance
                        • 1. Manage permissions and access
                          • 2. Configure row-level security (RLS) and object-level security
                            • 3. Apply data sensitivity labels and protection
                              - Manage workspaces and assets
                              • 1. Publish, update, and organize reports/datasets
                                • 2. Manage data refresh and scheduled updates
                                  • 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
                                          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. Reduce cardinality and data size
                                                    • 2. Use aggregations and composite models
                                                      • 3. Optimize relationships and calculations
                                                        - Create model calculations
                                                        • 1. Implement aggregation and time intelligence
                                                          • 2. Use DAX: calculated columns, calculated tables, measures
                                                            • 3. Use CALCULATE and filter context
                                                              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
                                                                    - Create reports and dashboards
                                                                    • 1. Design report layout, formatting, and interactivity
                                                                      • 2. Create dashboards and pinned elements
                                                                        • 3. Choose appropriate visualizations
                                                                          • 4. Apply filters, slicers, and drill-through
                                                                            - Enhance reports with analytics
                                                                            • 1. Apply conditional formatting and custom visuals
                                                                              • 2. Create paginated reports
                                                                                • 3. Use AI visuals, forecasts, and key influencers

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