Microsoft : PL-300 Deutsch Questions & Answers

Last Updated: Aug 25, 2026

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Microsoft Power BI Data Analyst Exam Certification Details:

Exam NameMicrosoft Certified - Power BI Data Analyst Associate
Duration180 mins
Number of Questions40-60
Exam Price$165 (USD)
Exam CodePL-300
Passing Score700 / 1000
Sample QuestionsMicrosoft Power BI Data Analyst Sample Questions
Schedule ExamPearson VUE
Books / TrainingCourse PL-300T00: Microsoft Power BI Data Analyst

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

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

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Visualize and analyze data25-30%- Perform data analysis
  • 1. Interpret results and identify trends
    • 2. Implement custom visuals and R/Python scripts
      • 3. Create and manage bookmarks and tooltips
        - Enhance reports with analytics
        • 1. Use AI visuals, forecasts, and key influencers
          • 2. Apply conditional formatting and custom visuals
            • 3. Create paginated reports
              - Create reports and dashboards
              • 1. Choose appropriate visualizations
                • 2. Design report layout, formatting, and interactivity
                  • 3. Create dashboards and pinned elements
                    • 4. Apply filters, slicers, and drill-through
                      Manage and secure Power BI15-20%- Manage workspaces and assets
                      • 1. Publish, update, and organize reports/datasets
                        • 2. Create and configure workspaces
                          • 3. Manage data refresh and scheduled updates
                            - Deploy and distribute content
                            • 1. Monitor usage and performance
                              • 2. Share reports, dashboards, and apps
                                • 3. Embed content in applications or websites
                                  - 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
                                        Prepare the data25-30%- Get or connect to data
                                        • 1. Choose storage modes: Import, DirectQuery, DirectLake
                                          • 2. Create and modify parameters
                                            • 3. Configure data source settings, credentials, and privacy levels
                                              • 4. Identify and connect to data sources or shared semantic models
                                                - Load and configure data
                                                • 1. Integrate data from multiple sources
                                                  • 2. Define table structures and properties
                                                    • 3. Configure data refresh settings
                                                      - Profile and clean data
                                                      • 1. Resolve inconsistencies, null values, and errors
                                                        • 2. Evaluate data statistics and column properties
                                                          • 3. Manage data types, columns, and rows
                                                            • 4. Transform data using Power Query Editor
                                                              Model the data25-30%- Design data models
                                                              • 1. Create common date tables
                                                                • 2. Implement role-playing dimensions
                                                                  • 3. Create relationships: cardinality, cross-filter direction, security
                                                                    • 4. Define tables, columns, and hierarchies
                                                                      - Create model calculations
                                                                      • 1. Use CALCULATE and filter context
                                                                        • 2. Implement aggregation and time intelligence
                                                                          • 3. Use DAX: calculated columns, calculated tables, measures
                                                                            - Optimize model performance
                                                                            • 1. Use aggregations and composite models
                                                                              • 2. Reduce cardinality and data size
                                                                                • 3. Optimize relationships and calculations

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