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


"Microsoft Power BI Data Analyst (PL-300 Deutsch Version)", also known as PL-300 exam, is a Microsoft Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 472 Q&As to your PL-300 Exam preparation. In the PL-300 exam resources, you will cover every field and category in Microsoft Certified: Power BI Data Analyst Associate Certification helping to ready you for your successful Microsoft Certification.

  • Exam Code: PL-300
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Total Questions: 472

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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:

  • Canada; CAD 70,000

  • Australia; AUD 75,000

  • United Kingdom; POUNDS 60,000

  • India; INR 500,000

  • United States; USD 90,000

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

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Manage and secure Power BI15-20%- Implement security and governance
  • 1. Configure row-level security (RLS) and object-level security
    • 2. Apply data sensitivity labels and protection
      • 3. Manage permissions and access
        - Manage workspaces and assets
        • 1. Manage data refresh and scheduled updates
          • 2. Create and configure workspaces
            • 3. Publish, update, and organize reports/datasets
              - Deploy and distribute content
              • 1. Monitor usage and performance
                • 2. Share reports, dashboards, and apps
                  • 3. Embed content in applications or websites
                    Prepare the data25-30%- Load and configure data
                    • 1. Define table structures and properties
                      • 2. Integrate data from multiple sources
                        • 3. Configure data refresh settings
                          - Get or connect to data
                          • 1. Identify and connect to data sources or shared semantic models
                            • 2. Create and modify parameters
                              • 3. Choose storage modes: Import, DirectQuery, DirectLake
                                • 4. Configure data source settings, credentials, and privacy levels
                                  - 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
                                          Visualize and analyze data25-30%- Create reports and dashboards
                                          • 1. Design report layout, formatting, and interactivity
                                            • 2. Choose appropriate visualizations
                                              • 3. Create dashboards and pinned elements
                                                • 4. Apply filters, slicers, and drill-through
                                                  - Perform data analysis
                                                  • 1. Create and manage bookmarks and tooltips
                                                    • 2. Implement custom visuals and R/Python scripts
                                                      • 3. Interpret results and identify trends
                                                        - Enhance reports with analytics
                                                        • 1. Use AI visuals, forecasts, and key influencers
                                                          • 2. Create paginated reports
                                                            • 3. Apply conditional formatting and custom visuals
                                                              Model the data25-30%- Optimize model performance
                                                              • 1. Reduce cardinality and data size
                                                                • 2. Optimize relationships and calculations
                                                                  • 3. Use aggregations and composite models
                                                                    - 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. Implement aggregation and time intelligence
                                                                              • 2. Use DAX: calculated columns, calculated tables, measures
                                                                                • 3. Use CALCULATE and filter context

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