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The need for this exam has increased tremendously over the years. As more people start using technology, there is a greater need for more professionals who can help them with their problems. With the increasing demand for people with these skills, it will be difficult for you to get a job without having one. If you want to succeed in your career, you should consider taking this test because it will give you an edge over other candidates who do not have it. It will also help you stand out from the crowd if you are applying for jobs online or even if you are looking for promotion within your company. Column data types have questions and answers subcategories for real time scenario. Sample questions about data source settings have calculated columns mobile view. Quick measure mode filters the app syllabus to practice the average sales. If you are feeling sensitive to additives in your food, you may want to format your schema prepared environment to be more additive-sensitive. This could involve avoiding foods that contain additives, eating organic foods whenever possible, or using natural sweeteners.

Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

  • Model the data (30-35%)
  • Deploy and maintain assets (20-25%)
  • Prepare the data (15-20%)
  • Visualize and analyze the data (25-30%)

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
Topic 1: Manage and secure Power BI15-20%- Implement security and governance
  • 1. Manage permissions and access
    • 2. Configure row-level security (RLS) and object-level security
      • 3. Apply data sensitivity labels and protection
        - 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. Embed content in applications or websites
                • 2. Monitor usage and performance
                  • 3. Share reports, dashboards, and apps
                    Topic 2: Visualize and analyze data25-30%- Enhance reports with analytics
                    • 1. Use AI visuals, forecasts, and key influencers
                      • 2. Apply conditional formatting and custom visuals
                        • 3. Create paginated reports
                          - Perform data analysis
                          • 1. Implement custom visuals and R/Python scripts
                            • 2. Interpret results and identify trends
                              • 3. Create and manage bookmarks and tooltips
                                - 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
                                        Topic 3: Prepare the data25-30%- Load and configure data
                                        • 1. Define table structures and properties
                                          • 2. Configure data refresh settings
                                            • 3. Integrate data from multiple sources
                                              - Profile and clean data
                                              • 1. Resolve inconsistencies, null values, and errors
                                                • 2. Transform data using Power Query Editor
                                                  • 3. Evaluate data statistics and column properties
                                                    • 4. Manage data types, columns, and rows
                                                      - Get or connect to data
                                                      • 1. Identify and connect to data sources or shared semantic models
                                                        • 2. Choose storage modes: Import, DirectQuery, DirectLake
                                                          • 3. Create and modify parameters
                                                            • 4. Configure data source settings, credentials, and privacy levels
                                                              Topic 4: Model the data25-30%- 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. Create common date tables
                                                                      • 2. Implement role-playing dimensions
                                                                        • 3. Define tables, columns, and hierarchies
                                                                          • 4. Create relationships: cardinality, cross-filter direction, security
                                                                            - Optimize model performance
                                                                            • 1. Use aggregations and composite models
                                                                              • 2. Optimize relationships and calculations
                                                                                • 3. Reduce cardinality and data size

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