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Microsoft PL-300 Deutsch Exam Braindumps - in .pdf Free Demo

  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Last Updated: Aug 08, 2026
  • Q & A: 515 Questions and Answers
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  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Last Updated: Aug 08, 2026
  • Q & A: 515 Questions and Answers
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Microsoft Power BI Data Analyst Exam Certification Details:

Passing Score700 / 1000
Exam NameMicrosoft Certified - Power BI Data Analyst Associate
Schedule ExamPearson VUE
Duration180 mins
Exam CodePL-300
Exam Price$165 (USD)
Sample QuestionsMicrosoft Power BI Data Analyst Sample Questions
Books / TrainingCourse PL-300T00: Microsoft Power BI Data Analyst
Number of Questions40-60

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

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Visualize the Data25-30%- Enrich reports with advanced visuals
  • 1. Use dynamic page navigation
  • 2. Create custom visuals
  • 3. Integrate Microsoft Copilot for Power BI
  • 4. Configure R and Python visuals
- Build visuals
  • 1. Format and configure pages and report backgrounds
  • 2. Determine the appropriate visual type
  • 3. Apply AI insights and enhanced analytics
  • 4. Apply sorting and filtering to visuals
  • 5. Configure data colors and shapes
  • 6. Configure visual interactions and cross-filtering
- Enhance reports for user experience
  • 1. Design for mobile and specific layouts
  • 2. Add tooltips and navigation
  • 3. Apply artistic formatting to visuals
  • 4. Add accessibility features and compliance
Prepare the Data10-15%- Clean, transform, and load the data
  • 1. Design and build intermediate tables
  • 2. Resolve data quality issues
  • 3. Determine proper data types
  • 4. Apply user-friendly naming conventions to columns and tables
  • 5. Identify the data structure
  • 6. Profile the data
  • 7. Configure data loading and queries
- Get data from various data sources
  • 1. Select a shared dataset or create a local copy
  • 2. Choose between DirectQuery, Import, and Dual storage modes
  • 3. Use Microsoft Fabric and OneLake
  • 4. Configure dataset refresh
  • 5. Identify and connect to a data source
  • 6. Change data source settings
Analyze the Data10-15%- Perform analysis in Power BI
  • 1. Use the Q&A visual
  • 2. Generate Pinn visual insights
  • 3. Configure auto-insights
  • 4. Apply Quick Insights
  • 5. Identify patterns and trends
- Work with metrics in Power BI
  • 1. Create metrics using Microsoft measurements
  • 2. Track goals with scorecards
  • 3. Define and monitor KPIs
  • 4. Use the Decomposition Tree visual
Model the Data25-30%- Create measures and DAX calculations
  • 1. Create semi-additive measures
  • 2. Write and use basic DAX syntax
  • 3. Create calculated measures
  • 4. Implement context transition
  • 5. Use Time Intelligence functions
  • 6. Use CALCULATE and FILTER functions
- Optimize model performance
  • 1. Optimize DirectQuery models
  • 2. Validate performance and resolve issues
  • 3. Optimize Dax queries
  • 4. Improve user experience by hiding objects
  • 5. Configure aggregations
- Develop the data model
  • 1. Build hierarchies and hierarchical fields
  • 2. Create calculated tables and columns
  • 3. Use the calculation group feature
  • 4. Implement row-level security roles
  • 5. Configure table relationships
  • 6. Create common date tables
- Design a data model
  • 1. Define tables and table schemas
  • 2. Implement row-level security
  • 3. Design a star schema or snowflake schema
  • 4. Apply field metadata and groupings
  • 5. Determine the appropriate granularity
  • 6. Resolve performance issues with relationships
Manage and Maintain Workspace Items10-15%- Create and manage workspaces
  • 1. Publish and update a Power BI app
  • 2. Endorse sensitive content with sensitivity labels
  • 3. Configure workspace membership and roles
  • 4. Configure content certification
- Manage datasets
  • 1. Manage gateways and data sources
  • 2. Resolve dataset permission issues
  • 3. Configure dataset metadata and properties
  • 4. Manage dataset refresh scheduling

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