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

PL-300 Deutsch
  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Aug 18, 2026
  • Q & A: 472 Questions and Answers

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Prerequisites of Microsoft Power BI Data Analyst Certification Exam

The Microsoft Power BI Data Analyst Certification Exam is a test which assesses your ability to have a high-level understanding of the data analysis process, including collecting, analyzing and visualizing data. It is a certification that will help you gain the required skills to be able to work with business intelligence solutions and tools. To be eligible to take this exam, you must meet the following prerequisites:

  • You have at least one year of experience working with relational databases, such as SQL Server or Oracle. In addition, you should have a basic understanding of how to create queries and reports using SQL Server Management Studio or Toad Data Point.

  • You are a professional data analyst with 2-4 years of work experience or an IT professional with 2-4 years of experience.

  • You have at least one year of experience using Power BI Desktop.

  • You have at least one year of experience using Power Query in Excel, or an equivalent experience using another data analysis tool.

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

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Microsoft Power BI Data Analyst Certification Exam Reference

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Visualize and analyze data25-35%- Create reports and dashboards
  • 1. Create visuals and KPIs
    • 2. Design report layouts
      - Enhance reports for usability
      • 1. Implement drill-through and navigation
        • 2. Apply filters and slicers
          Topic 2: Deploy and maintain assets15-20%- Manage workspaces and datasets
          • 1. Configure refresh schedules
            • 2. Publish and manage datasets
              - Secure and monitor Power BI
              • 1. Implement row-level security
                • 2. Monitor usage and performance
                  Topic 3: Model the data30-40%- Develop a data model
                  • 1. Create DAX calculations
                    • 2. Optimize model performance
                      - Design a data model
                      • 1. Define relationships and cardinality
                        • 2. Design star schema
                          Topic 4: Prepare the data15-20%- Get data from data sources
                          • 1. Identify and connect to data sources
                            • 2. Select storage modes
                              - Clean, transform, and load data
                              • 1. Shape and transform data
                                • 2. Clean data using Power Query

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