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Microsoft PL-300 exam is an excellent opportunity for individuals who want to validate their skills and knowledge in using Power BI to analyze and visualize data. With the demand for data analysts increasing across industries, earning this certification can help individuals stand out in a competitive job market and open up new career opportunities.

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

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Microsoft PL-300 exam, also known as the Microsoft Power BI Data Analyst exam, is designed for individuals who want to demonstrate their skills in using Microsoft Power BI for data analysis. PL-300-Deutsch exam is intended for data analysts and business intelligence professionals who use Power BI for data visualization, reporting, and data analysis. PL-300-Deutsch exam is a part of the Microsoft Power Platform certification program, which also includes other exams such as the Power Apps Developer Exam and the Power Platform Functional Consultant Exam.

Microsoft PL-300 Deutsch Exam Syllabus Topics:

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

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