PL-300 Deutsch exam dumps

Microsoft PL-300 Deutsch Value Package

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  • Exam Code: PL-300 Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • No. of Questions: 472 Questions and Answers
  • Updated: Aug 19, 2026

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What is the exam cost of the Microsoft Power BI Data Analyst Certification Exam

The cost of the Microsoft Power BI Data Analyst Certification Exam is 165 USD.

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

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The steps to follow for the registration of the Microsoft Power BI Data Analyst Certification Exam are as follows:

  • Go to the official website of Microsoft, and click on “Microsoft Learning”.

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  • You will see a list of courses that are available on this website. Select “Microsoft Power BI Data Analyst Certification Exam” and click on “Enroll Now” button.

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Microsoft PL-300 Deutsch Exam Syllabus Topics:

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

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