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Last Updated: Aug 16, 2026

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

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Microsoft Power BI Data Analyst Exam Certification Details:

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

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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

  • Developing dashboards that provide actionable insights into business performance

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

  • Understanding business context, including organizational strategy and objectives

Microsoft PL-300 Exam Syllabus Topics:

SectionWeightObjectives
Visualize and analyze data25-30%- 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. Interpret results and identify trends
          • 2. Create and manage bookmarks and tooltips
            • 3. Implement custom visuals and R/Python scripts
              - Create reports and dashboards
              • 1. Apply filters, slicers, and drill-through
                • 2. Choose appropriate visualizations
                  • 3. Design report layout, formatting, and interactivity
                    • 4. Create dashboards and pinned elements
                      Prepare the data25-30%- Profile and clean data
                      • 1. Manage data types, columns, and rows
                        • 2. Resolve inconsistencies, null values, and errors
                          • 3. Transform data using Power Query Editor
                            • 4. Evaluate data statistics and column properties
                              - Get or connect to data
                              • 1. Configure data source settings, credentials, and privacy levels
                                • 2. Create and modify parameters
                                  • 3. Identify and connect to data sources or shared semantic models
                                    • 4. Choose storage modes: Import, DirectQuery, DirectLake
                                      - Load and configure data
                                      • 1. Configure data refresh settings
                                        • 2. Integrate data from multiple sources
                                          • 3. Define table structures and properties
                                            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. Embed content in applications or websites
                                                    • 2. Monitor usage and performance
                                                      • 3. Share reports, dashboards, and apps
                                                        - Implement security and governance
                                                        • 1. Configure row-level security (RLS) and object-level security
                                                          • 2. Apply data sensitivity labels and protection
                                                            • 3. Manage permissions and access
                                                              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. Implement aggregation and time intelligence
                                                                      • 2. Use CALCULATE and filter context
                                                                        • 3. Use DAX: calculated columns, calculated tables, measures
                                                                          - 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

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