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  • Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian

  • Time Duration: 180 minutes

  • The passing score: 700

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The Microsoft certification exams are considered to be the toughest in the industry. This is because of the fact that if you have passed one of these exams, then it means that you have gone through a tough time and have shown that you are capable enough to handle any situation. Microsoft Power BI Data Analyst Certification Exam is designed to give you a hard time. It won't let up until you finally give up entirely and surrender your certification. TrainingDump Microsoft PL-300 Dumps are the only way to pass your exam and get certified in your first attempt.

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

Microsoft PL-300 Deutsch Exam Syllabus Topics:

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

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