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, UpdateDumps 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.

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  • Updated on: Aug 22, 2026
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  • Updated on: Aug 22, 2026
  • No. of Questions: 472 Questions & 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:

  • United States; USD 90,000

  • Australia; AUD 75,000

  • Canada; CAD 70,000

  • India; INR 500,000

  • United Kingdom; POUNDS 60,000

Exam PL-300: Microsoft Power BI Data Analyst

The Power BI data analyst delivers actionable insights by leveraging available data and applying domain expertise. The Power BI data analyst collaborates with key stakeholders across verticals to identify business requirements, cleans and transforms the data, and then designs and builds data models by using Power BI. The Power BI data analyst provides meaningful business value through easy-to-comprehend data visualizations, enables others to perform self-service analytics, and deploys and configures solutions for consumption. Candidates for this exam should be proficient using Power Query and writing expressions by using DAX.

Passing score: 700. Learn more about exam scores.

Part of the requirements for: Microsoft Certified: Power BI Data Analyst Associate

Benefits

The Microsoft Power BI Data Analyst certification is a globally recognized certification that validates your ability to design, build, and optimize data models using Power BI. It also demonstrates your expertise in building queries and reports with appropriate visuals, including dashboards, scorecards, and visualizations. This certification focuses on the intermediate-level skills required for data modeling and analysis.

The Microsoft Power BI Data Analyst certification provides many benefits to you. It helps you boost your career growth by validating your knowledge in building business intelligence solutions using Power BI platform. It validates your skills in designing reports and visualizations for various business needs. It increases your credibility among employers as most companies look for professionals who have this certification. It provides an opportunity to earn a higher salary package compared to those without this certification. UpdateDumps Microsoft PL-300 Dumps will help you to pass your certification exam and get highly paid job.

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

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Microsoft Power BI Data Analyst Exam Certification Details:

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

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

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