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Last Updated: Jul 26, 2026

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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. Test4Sure 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 PL-300日本語 Exam Syllabus Topics:

SectionWeightObjectives
Manage and secure Power BI15-20%- Deploy and distribute content
  • 1. Monitor usage and performance
    • 2. Embed content in applications or websites
      • 3. Share reports, dashboards, and apps
        - 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. Manage permissions and access
                • 2. Apply data sensitivity labels and protection
                  • 3. Configure row-level security (RLS) and object-level security
                    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. Transform data using Power Query Editor
                            • 2. Manage data types, columns, and rows
                              • 3. Resolve inconsistencies, null values, and errors
                                • 4. Evaluate data statistics and column properties
                                  - Get or connect to data
                                  • 1. Identify and connect to data sources or shared semantic models
                                    • 2. Create and modify parameters
                                      • 3. Configure data source settings, credentials, and privacy levels
                                        • 4. Choose storage modes: Import, DirectQuery, DirectLake
                                          Model the data25-30%- Optimize model performance
                                          • 1. Reduce cardinality and data size
                                            • 2. Optimize relationships and calculations
                                              • 3. Use aggregations and composite models
                                                - Create model calculations
                                                • 1. Implement aggregation and time intelligence
                                                  • 2. Use DAX: calculated columns, calculated tables, measures
                                                    • 3. Use CALCULATE and filter context
                                                      - 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
                                                              Visualize and analyze data25-30%- 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
                                                                      - 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
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