Exam Code: PL-300
Exam Name: Microsoft Power BI Data Analyst
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Power BI Data Analyst Associate

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PL-300

Last Updated: Sep 14, 2026

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What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam

The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.

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

Passing Score, Duration & Questions For Microsoft PL-300 Exam

  • Time Duration: 180 minutes

  • Number of Questions: 40-60 questions

  • The passing score: 700

  • Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian

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Microsoft Power BI Data Analyst Exam Certification Details:

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

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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

  • United States; USD 90,000

  • India; INR 500,000

Microsoft PL-300 Exam Syllabus Topics:

SectionWeightObjectives
Model the data25-30%- Design data models
  • 1. Create relationships: cardinality, cross-filter direction, security
    • 2. Create common date tables
      • 3. Define tables, columns, and hierarchies
        • 4. Implement role-playing dimensions
          - 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. Use DAX: calculated columns, calculated tables, measures
                    • 3. Implement aggregation and time intelligence
                      Manage and secure Power BI15-20%- Manage workspaces and assets
                      • 1. Manage data refresh and scheduled updates
                        • 2. Publish, update, and organize reports/datasets
                          • 3. Create and configure workspaces
                            - Deploy and distribute content
                            • 1. Share reports, dashboards, and apps
                              • 2. Monitor usage and performance
                                • 3. Embed content in applications or websites
                                  - 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
                                        Visualize and analyze data25-30%- 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. Create paginated reports
                                                  • 2. Use AI visuals, forecasts, and key influencers
                                                    • 3. Apply conditional formatting and custom visuals
                                                      - Perform data analysis
                                                      • 1. Interpret results and identify trends
                                                        • 2. Create and manage bookmarks and tooltips
                                                          • 3. Implement custom visuals and R/Python scripts
                                                            Prepare the data25-30%- Profile and clean data
                                                            • 1. Resolve inconsistencies, null values, and errors
                                                              • 2. Manage data types, columns, and rows
                                                                • 3. Evaluate data statistics and column properties
                                                                  • 4. Transform data using Power Query Editor
                                                                    - Load and configure data
                                                                    • 1. Configure data refresh settings
                                                                      • 2. Define table structures and properties
                                                                        • 3. Integrate data from multiple sources
                                                                          - Get or connect to data
                                                                          • 1. Configure data source settings, credentials, and privacy levels
                                                                            • 2. Identify and connect to data sources or shared semantic models
                                                                              • 3. Create and modify parameters
                                                                                • 4. Choose storage modes: Import, DirectQuery, DirectLake

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