Microsoft PL-300 Deutsch Q&A - in .pdf

  • Exam Code: PL-300 Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Jul 22, 2026
  • Q & A: 515 Questions and Answers
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Microsoft PL-300 Deutsch Q&A - Testing Engine

  • Exam Code: PL-300 Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Jul 22, 2026
  • Q & A: 515 Questions and Answers
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About Microsoft PL-300 Deutsch Exam

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:

  • Developing dashboards that provide actionable insights into business performance

  • Understanding business context, including organizational strategy and objectives

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

  • Creating custom data models that support analysis and reporting requirements

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

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Microsoft Power BI Data Analyst Certification Exam Reference

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

  • Australia; AUD 75,000

  • United Kingdom; POUNDS 60,000

  • United States; USD 90,000

  • Canada; CAD 70,000

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

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

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