Free Practice Questions for Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) Certification
- Exam guide version:
- Skills measured as of October 19, 2026
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 4 Mar 2026
- Question bank last updated:
- 12 Oct 2026
Study with 301 exam-style practice questions designed to help you prepare for the Microsoft Certified: Fabric Analytics Engineer Associate (DP-600). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Exam Details
Key information about Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
- Multiple choice
- Ordering
- True/False
English, with localized versions available approximately eight weeks after English updates.
Professionals with subject matter expertise in designing, creating, and managing analytical assets like semantic models, warehouses, or lakehouses, proficient in SQL, KQL, and DAX.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Maintain a data analytics solution
1.1: Implement security and governance
- Implement workspace-level access controls - Implement item-level access controls - Implement row-level, column-level, object-level, and file-level access control - Apply sensitivity labels to items - Endorse items
1.2: Maintain the analytics development lifecycle
- Configure version control for a workspace - Create and manage a Power BI Desktop project (.pbip) - Create and configure deployment pipelines - Perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models - Deploy and manage semantic models by using the XMLA endpoint - Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
2: Prepare data
2.1: Get data
- Create a data connection - Discover data by using OneLake catalog and Real-Time hub - Ingest or access data as needed - Choose between different data stores - Implement OneLake integration for Eventhouse and semantic models
2.2: Transform data
- Create views, functions, and stored procedures - Enrich data by adding new columns or tables - Implement a star schema for a lakehouse or warehouse - Denormalize data - Aggregate data - Merge or join data - Identify and resolve duplicate data, missing data, or null values - Convert column data types - Filter data
2.3: Query and analyze data
- Select, filter, and aggregate data by using the Visual query editor - Select, filter, and aggregate data by using SQL - Select, filter, and aggregate data by using KQL - Select, filter, and aggregate data by using DAX
3: Implement and manage semantic models
3.1: Design and build semantic models
- Choose a storage mode - Implement a star schema for a semantic model - Implement relationships, such as bridge tables and many-to-many relationships - Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions - Implement calculation groups, dynamic format strings, and field parameters - Identify use cases for and configure large semantic model storage format - Design and build composite models
3.2: Optimize enterprise-scale semantic models
- Implement performance improvements in queries and report visuals - Improve DAX performance - Configure Direct Lake, including default fallback and refresh behavior - Choose between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint - Implement incremental refresh for semantic models
Techniques & products