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

PL-300 Deutsch Exam Questions
  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Sep 09, 2026
  • Q & A: 472 Questions and Answers
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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 are the special features of the Microsoft Power BI Data Analyst Certification Exam

The Microsoft Power BI Data Analyst Certification Exam will help you prepare for the new job role of a data analyst. A certified Data Analyst can use this exam to prove their skills in using Power BI to analyze business data, generate insightful visualizations, and share them with customers. Microsoft PL-300 Dumps is designed to test your knowledge of business data modeling, data analysis and visualization, and dashboard development in Power BI. The exam will cover the following topics:

  • Creating custom data models that support analysis and reporting requirements

  • Understanding business context, including organizational strategy and objectives

  • Identifying sources of data that can contribute to achieving business goals

  • Developing dashboards that provide actionable insights into business performance

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

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