Microsoft's skills measured for DP-600 as of October 19, 2026, mapped to the chapters of Ultra Transcenders DP-600: Implementing Analytics Solutions Using Microsoft Fabric.
| Objective | Chapters |
|---|---|
| Maintain a data analytics solution (25–30%) | |
| Implement security and governance | 2. Workspace and item access control; 3. Row-, column-, object- and file-level security; 4. Governance: sensitivity labels, endorsement, lineage and impact analysis |
| Maintain the analytics development lifecycle | 4. Governance: sensitivity labels, endorsement, lineage and impact analysis; 5. Version control, Power BI projects and deployment pipelines; 6. XMLA endpoint, shared semantic models and reusable assets |
| Prepare data (45–50%) | |
| Get data | 1. Microsoft Fabric for analytics engineers: platform, capacities and choosing a data store; 4. Governance: sensitivity labels, endorsement, lineage and impact analysis; 7. Getting data into Fabric: connections, OneLake catalog, shortcuts and ingestion; 8. Lakehouses, Delta tables and Spark transformations; 9. Data warehouses and T-SQL in Fabric; 11. Power Query, Dataflow Gen2 and the visual query editor; 12. Real-Time Intelligence and KQL |
| Transform data | 8. Lakehouses, Delta tables and Spark transformations; 9. Data warehouses and T-SQL in Fabric; 10. Star schemas and data preparation patterns; 11. Power Query, Dataflow Gen2 and the visual query editor; 12. Real-Time Intelligence and KQL |
| Query and analyze data | 9. Data warehouses and T-SQL in Fabric; 11. Power Query, Dataflow Gen2 and the visual query editor; 12. Real-Time Intelligence and KQL; 14. DAX for analytics engineers |
| Implement and manage semantic models (25–30%) | |
| Design and build semantic models | 6. XMLA endpoint, shared semantic models and reusable assets; 13. Designing semantic models: storage modes, relationships and composite models; 14. DAX for analytics engineers |
| Optimize enterprise-scale semantic models | 3. Row-, column-, object- and file-level security; 8. Lakehouses, Delta tables and Spark transformations; 14. DAX for analytics engineers; 15. Optimising enterprise semantic models: performance, Direct Lake and incremental refresh |
The full list of tasks under each objective is in the official DP-600 study guide and practice assessment. The book is organised by technology, so one chapter often serves several objectives.