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Analyzing Data with Microsoft Power BI (DA-100 Deutsch Version): DA-100 Exam

"Analyzing Data with Microsoft Power BI (DA-100 Deutsch Version)", also known as DA-100 exam, is a Microsoft Certification. With the complete collection of questions and answers, PrepAwayPDF has assembled to take you through 150 Q&As to your DA-100 Exam preparation. In the DA-100 exam resources, you will cover every field and category in Microsoft Certified: Data Analyst Associate Certification helping to ready you for your successful Microsoft Certification.

  • Exam Code: DA-100
  • Exam Name: Analyzing Data with Microsoft Power BI (DA-100 Deutsch Version)
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Certified: Data Analyst Associate
  • Updated: Jul 21, 2026
  • No. of Questions: 150 Questions & Answers with Testing Engine
  • Download Limit: Unlimited
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The Microsoft DA-100 exam (Analyzing Data with Microsoft Power BI) is intended for data analysts and is the only test required to obtain the Microsoft Certified: Data Analyst Associate certificate.

Conclusion: Getting Microsoft Certified Is Challenging But Possible!

The Microsoft Certified: Data Analyst Associate certificate will help secure a career as a data analyst, which will come with it a high paycheck and different career opportunities. However, achieving this certification won’t be easy as one needs to overcome DA-100 (Analyzing Data with Microsoft Power BI) exam. But, with a huge number of study guides available on Amazon and the official MS training course, passing this test is going to be that much easier.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100

Microsoft DA-100 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Model the data30%- Optimize model performance
  • 1. Improve performance by identifying and removing unnecessary rows and columns
  • 2. Optimize Power Query transformations
  • 3. Improve performance by choosing optimal data types
- Create measures by using DAX
  • 1. Implement Time Intelligence using DAX
  • 2. Create semi-additive measures
  • 3. Create calculated measures
  • 4. Use DAX aggregation functions
  • 5. Use DAX logical functions
- Develop a data model
  • 1. Implement row-level security roles
  • 2. Create hierarchies
  • 3. Create calculated columns
  • 4. Create calculated tables
- Design a data model
  • 1. Define the tables
  • 2. Design and implement role-playing dimensions
  • 3. Define a relationship's cross-filter direction and security filtering
  • 4. Configure table and column properties
  • 5. Define quick measures
Visualize the data25%- Create reports
  • 1. Add visualization items to reports
  • 2. Import a custom visual
  • 3. Choose an appropriate visualization type
  • 4. Format and configure visualizations
  • 5. Configure conditional formatting
  • 6. Create and manage bookmarks
  • 7. Apply slicing and filtering
- Create dashboards
  • 1. Manage tiles on a dashboard
  • 2. Configure data alerts
  • 3. Use the Q&A feature
  • 4. Set mobile view
- Enforce accessibility
  • 1. Design accessible reports
  • 2. Use accessibility features
  • 3. Configure tab order
Analyze the data15%- Enhance reports for usability and storytelling
  • 1. Configure sync slicers
  • 2. Edit and configure interactions between visuals
  • 3. Group and layer visuals
  • 4. Apply sorting
  • 5. Create custom tooltips
  • 6. Configure navigation for a report
  • 7. Configure bookmarks
- Perform advanced analysis
  • 1. Use advanced analytical custom visuals
  • 2. Perform What-if analysis
  • 3. Use the Analyze feature
  • 4. Identify outliers
Deploy and maintain deliverables10%- Manage datasets
  • 1. Configure cross-workspace sharing
  • 2. Provide access to datasets
  • 3. Configure row-level security group membership
  • 4. Configure a dataset scheduled refresh
- Create and manage workspaces
  • 1. Create and configure a workspace
  • 2. Assign workspace roles
  • 3. Configure and update a workspace app
Prepare the data20%- Get data from different data sources
  • 1. Change data source settings
  • 2. Select a storage mode
  • 3. Identify and connect to a data source
  • 4. Choose an appropriate Power BI data source
  • 5. Select a shared dataset or create a local dataset
- Profile the data
  • 1. Examine data structures
  • 2. Identify data anomalies
  • 3. Interpret data distribution and variance
- Transform and load the data
  • 1. Define relationship cardinality and cross-filter direction
  • 2. Create and transform columns
  • 3. Select appropriate column data types
  • 4. Design a star schema
  • 5. Create and transform tables
- Clean the data
  • 1. Resolve data import errors
  • 2. Evaluate data, including data statistics and column properties
  • 3. Resolve inconsistencies, unexpected or null values, and data quality issues

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