Microsoft DP-600 Exam Details & Actual Exam Questions

  • Exam Code/Number: DP-600
  • Exam Name/Title: Implementing Analytics Solutions Using Microsoft Fabric
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Certified
  • Exam Questions: 194
  • Updated On: Jul,07 2026
  • Certification Level: Associate

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Exam Questions

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Microsoft DP-600 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Analytics Solutions Using Microsoft Fabric
Exam Number:DP-600
Certificate Validity Period:12 months (renewable via free online assessment)
Passing Score:700 (scale 100-1000)
Exam Format:Multiple choice, Multiple response, Yes/No, Drag & drop, Case study scenario questions
Available Languages:English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil)
Exam Duration:100-120
Exam Price:$165 USD
Real Exam Qty:40-60
Sample Questions:Microsoft DP-600 Sample Questions
Exam Way:Online proctored remote exam or onsite testing center exam
Pre Condition:No mandatory prerequisite exams; recommended experience with T-SQL, DAX, Power BI, data modeling and Microsoft Fabric core services
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-600

Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Maintain a data analytics solution25-30%- Implement security and governance
  • 1. Configure workspace-level access controls
    • 2. Apply row-level, column-level, object-level and file-level security
      • 3. Manage sensitivity labels and item endorsements
        • 4. Configure item-level access controls
          - Manage analytics development lifecycle
          • 1. Create and manage Power BI Desktop project (.pbip)
            • 2. Set up workspace version control
              • 3. Configure and use deployment pipelines
                Prepare data45-50%- Create lakehouse and warehouse objects
                • 1. Create warehouse tables and views
                  • 2. Design lakehouse table structures
                    - Optimize data storage and query performance
                    • 1. Implement partitioning and indexing
                      • 2. Optimize Spark and SQL query execution
                        - Ingest and transform data
                        • 1. Build and manage data pipelines
                          • 2. Clean and transform data with Dataflows and Notebooks
                            Implement and manage semantic models25-30%- Perform exploratory analytics and data querying
                            • 1. Query data with SQL, Kusto and Spark
                              • 2. Build visual analytics assets in Power BI
                                - Optimize enterprise-scale semantic models
                                • 1. Optimize model refresh performance
                                  • 2. Reduce model size and memory consumption
                                    - Design and build semantic models
                                    • 1. Create tables, relationships and calculated columns
                                      • 2. Develop DAX measures and calculation groups


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