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Skills measured

  • Prepare the data (15-20%)
  • Download the study guide in the preceding “Tip” box for more details about the skills measured on this exam.
  • Model the data (30-35%)
  • Deploy and maintain assets (20-25%)
  • Visualize and analyze the data (25-30%)

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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 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.

  • 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 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.

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

Microsoft PL-300 Korean Exam Syllabus Topics:

SectionWeightObjectives
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. Configure data source settings, credentials, and privacy levels
                  • 2. Create and modify parameters
                    • 3. Choose storage modes: Import, DirectQuery, DirectLake
                      • 4. Identify and connect to data sources or shared semantic models
                        Visualize and analyze data25-30%- Perform data analysis
                        • 1. Implement custom visuals and R/Python scripts
                          • 2. Create and manage bookmarks and tooltips
                            • 3. Interpret results and identify trends
                              - Create reports and dashboards
                              • 1. Choose appropriate visualizations
                                • 2. Design report layout, formatting, and interactivity
                                  • 3. Create dashboards and pinned elements
                                    • 4. Apply filters, slicers, and drill-through
                                      - Enhance reports with analytics
                                      • 1. Use AI visuals, forecasts, and key influencers
                                        • 2. Apply conditional formatting and custom visuals
                                          • 3. Create paginated reports
                                            Manage and secure Power BI15-20%- Manage workspaces and assets
                                            • 1. Publish, update, and organize reports/datasets
                                              • 2. Create and configure workspaces
                                                • 3. Manage data refresh and scheduled updates
                                                  - Deploy and distribute content
                                                  • 1. Monitor usage and performance
                                                    • 2. Embed content in applications or websites
                                                      • 3. Share reports, dashboards, and apps
                                                        - Implement security and governance
                                                        • 1. Apply data sensitivity labels and protection
                                                          • 2. Configure row-level security (RLS) and object-level security
                                                            • 3. Manage permissions and access
                                                              Model the data25-30%- Create model calculations
                                                              • 1. Use CALCULATE and filter context
                                                                • 2. Use DAX: calculated columns, calculated tables, measures
                                                                  • 3. Implement aggregation and time intelligence
                                                                    - Optimize model performance
                                                                    • 1. Reduce cardinality and data size
                                                                      • 2. Optimize relationships and calculations
                                                                        • 3. Use aggregations and composite models
                                                                          - Design data models
                                                                          • 1. Create common date tables
                                                                            • 2. Define tables, columns, and hierarchies
                                                                              • 3. Implement role-playing dimensions
                                                                                • 4. Create relationships: cardinality, cross-filter direction, security

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