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

Microsoft PL-300 Deutsch test insides dumps
  • Exam Code: PL-300-Deutsch
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Deutsch Version)
  • Updated: Sep 18, 2026
  • Q & A: 472 Questions and Answers
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Exam PL-300: Microsoft Power BI Data Analyst

The Power BI data analyst delivers actionable insights by leveraging available data and applying domain expertise. The Power BI data analyst collaborates with key stakeholders across verticals to identify business requirements, cleans and transforms the data, and then designs and builds data models by using Power BI. The Power BI data analyst provides meaningful business value through easy-to-comprehend data visualizations, enables others to perform self-service analytics, and deploys and configures solutions for consumption. Candidates for this exam should be proficient using Power Query and writing expressions by using DAX.

Passing score: 700. Learn more about exam scores.

Part of the requirements for: Microsoft Certified: Power BI Data Analyst Associate

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

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Introduction of Microsoft PL-300 Exam

Microsoft offers a wide range of certifications to enhance your knowledge in various areas of IT. The Microsoft Power BI Data Analyst Certification Exam is one of the most popular and sought-after Microsoft certification exams which aims to develop your skills as a data analyst by providing you with an opportunity to learn about basic business intelligence concepts, data analysis techniques and tools that can be used for building data models. Microsoft PL-300 Dumps are the best way to pass your exam.

The Microsoft certification exams enable you to validate your knowledge and skills in the system. If you have a job, it helps you to get promoted. The certification is a must-have for IT professionals. Microsoft Power BI Data Analyst Certification Exam is an exam designed by Microsoft for data analysts who are interested in gaining knowledge of the Microsoft Azure cloud platform and Power BI suite tools. The exam tests your ability to design, develop, and implement analytical solutions using Excel, Power Pivot, Power View and other related tools.

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 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 BI Desktop.

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

  • 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.

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

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