Microsoft Power BI Data Analyst (PL-300 Deutsch Version) : PL-300 Deutsch test torrent

PL-300 Deutsch Exam Simulator
  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Jul 28, 2026
  • Q & A: 515 Questions and Answers

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Prerequisites of Microsoft Power BI Data Analyst Certification Exam

The Microsoft Power BI Data Analyst Certification Exam is a test which assesses your ability to have a high-level understanding of the data analysis process, including collecting, analyzing and visualizing data. It is a certification that will help you gain the required skills to be able to work with business intelligence solutions and tools. To be eligible to take this exam, you must meet the following prerequisites:

  • You have at least one year of experience working with relational databases, such as SQL Server or Oracle. In addition, you should have a basic understanding of how to create queries and reports using SQL Server Management Studio or Toad Data Point.

  • You have at least one year of experience using Power BI Desktop.

  • You are a professional data analyst with 2-4 years of work experience or an IT professional with 2-4 years of experience.

  • You have at least one year of experience using Power Query in Excel, or an equivalent experience using another data analysis tool.

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Skills measured

  • Download the study guide in the preceding “Tip” box for more details about the skills measured on this exam.
  • Deploy and maintain assets (20-25%)
  • Model the data (30-35%)
  • 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 Deutsch Exam Syllabus Topics:

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

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