Microsoft Fabric Analytics Engineer (DP-600) Exam Prep

What's inside

1. Level 1: DP-600 Orientation: Exam, Scoring and Study PlanFree
   [All sections] Learn how DP-600 works: you need 700 to pass, the three skill areas in the skills measured as of October 19, 2026 are weighted 25–30%, 45–50% and 25–30%, and scenario 'best answer' questions have a method for reading them. Microsoft's study guide is free on Microsoft Learn and not reproduced here, and this course is independent of Microsoft, not endorsed by it, and cannot promise a pass.
2. Level 2: Workspace-Level Access ControlsFree
   [Maintain a data analytics solution – Implement security and governance] Learn how the Fabric workspace roles Admin, Member, Contributor and Viewer control what users and groups can create, edit, share and read across every item in a workspace.
3. Level 3: Item-Level Access Controls
   [Maintain a data analytics solution – Implement security and governance] Learn to share single items such as lakehouses, warehouses and semantic models without giving workspace roles, using item permissions such as Read, ReadData, ReadAll, Build, Write and Reshare.
4. Level 4: Row-Level Security in Warehouses and Semantic Models
   [Maintain a data analytics solution – Implement security and governance] Learn to implement row-level access control with T-SQL security policies and predicate functions in a warehouse or SQL analytics endpoint, and with DAX-based roles in a semantic model.
5. Level 5: Column-Level and Object-Level Security
   [Maintain a data analytics solution – Implement security and governance] Learn to restrict columns with T-SQL GRANT/DENY in a warehouse and to hide tables and columns from semantic model roles with object-level security (OLS), including dynamic data masking as the related warehouse option.
6. Level 6: File-Level Access with OneLake Security
   [Maintain a data analytics solution – Implement security and governance] Learn file-level and folder-level access control in OneLake using OneLake security (data access) roles on lakehouse Tables and Files folders, and how those roles carry through shortcuts and Fabric engines.
7. Level 7: Sensitivity Labels and Endorsement
   [Maintain a data analytics solution – Implement security and governance] Learn to apply Microsoft Purview sensitivity labels to Fabric items and understand how labels pass to downstream items and exports. Also learn to endorse items as Promoted or Certified, or mark them as Master data, and who is allowed to do each.
8. Level 8: Configure Version Control for a Workspace
   [Maintain a data analytics solution – Maintain the analytics development lifecycle] Learn Fabric Git integration: connecting a workspace to an Azure DevOps or GitHub repository and branch, committing and updating, resolving conflicts, and using branching patterns for development.
9. Level 9: Power BI Desktop Projects (.pbip)
   [Maintain a data analytics solution – Maintain the analytics development lifecycle] Learn to create and manage a Power BI Desktop project (.pbip): its folder structure, the TMDL semantic model definition and the PBIR report format, and why text-based files make source control and code review possible.
10. Level 10: Create and Configure Deployment Pipelines
    [Maintain a data analytics solution – Maintain the analytics development lifecycle] Learn to create a deployment pipeline, assign workspaces to its stages, compare and deploy content between stages, and configure deployment rules for data sources and parameters.
11. Level 11: Impact Analysis of Downstream Dependencies
    [Maintain a data analytics solution – Maintain the analytics development lifecycle] Learn to perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows and semantic models using lineage view and the impact analysis pane, and to notify affected contacts before a breaking change.
12. Level 12: Deploy and Manage Models via the XMLA Endpoint
    [Maintain a data analytics solution – Maintain the analytics development lifecycle] Learn to enable XMLA read-write, connect from tools such as SQL Server Management Studio and Tabular Editor, and deploy, script, refresh and manage semantic models through the XMLA endpoint.
13. Level 13: Reusable Assets: .pbit, .pbids and Shared Models
    [Maintain a data analytics solution – Maintain the analytics development lifecycle] Learn to create and update reusable assets: Power BI template (.pbit) files with parameters, Power BI data source (.pbids) files that pre-set connections, and shared semantic models that many reports use through live connections.
14. Level 14: Create a Data Connection
    [Prepare data – Get data] Learn to create a data connection in Fabric through Manage connections and gateways, choosing between cloud connections, an on-premises data gateway and a virtual network data gateway, and setting authentication methods and privacy levels.
15. Level 15: Discover Data with OneLake Catalog and Real-Time Hub
    [Prepare data – Get data] Learn to discover data using the OneLake catalog, which lets you explore, filter by endorsement and domain, and request access, and the Real-Time hub, which lets you browse data streams, Microsoft sources and Fabric events.
16. Level 16: Ingest Data with Pipelines and Copy Job
    [Prepare data – Ingest or access data as needed] Learn to ingest data with Data Factory pipelines (the Copy activity and its orchestration) and with Copy job, including full versus incremental copy and loading into lakehouse or warehouse tables.
17. Level 17: Ingest Data with Dataflows Gen2 and Notebooks
    [Prepare data – Ingest or access data as needed] Learn to ingest and shape data with Dataflows Gen2 (Power Query Online with a data destination) and with Spark notebooks, and when each one suits a team's skills and the volume of data.
18. Level 18: Access Data in Place: Shortcuts and Mirroring
    [Prepare data – Ingest or access data as needed] Learn to access data without building copy processes, using OneLake shortcuts to OneLake, ADLS Gen2, Amazon S3 and other sources, and database mirroring that replicates operational databases into OneLake as Delta tables.
19. Level 19: Ingest Streaming Data with Eventstreams
    [Prepare data – Ingest or access data as needed] Learn to ingest real-time data with eventstreams: adding sources, applying basic event processing, and routing to destinations such as an Eventhouse KQL database or a lakehouse.
20. Level 20: Choose Between Lakehouse, Warehouse and Eventhouse
    [Prepare data – Choose between different data stores] Learn to choose between a lakehouse, a warehouse and an Eventhouse based on data shape, team skills (Spark, T-SQL, KQL), write patterns, latency and multi-table transaction needs, as exam scenario questions demand.
21. Level 21: OneLake Integration for Eventhouse and Semantic Models
    [Prepare data – Get data] Learners turn on OneLake availability for an Eventhouse (KQL database) and OneLake integration for an import semantic model, so both expose their data as Delta tables. They also learn when doing this beats copying the data.
22. Level 22: Creating Views in Warehouse and SQL Endpoint
    [Prepare data – Transform data] Learners create T-SQL views in a Fabric warehouse and in a lakehouse's SQL analytics endpoint to reshape data for downstream use. They also learn which objects each item lets you create.
23. Level 23: Functions and Stored Procedures in T-SQL
    [Prepare data – Transform data] Learners write inline table-valued functions and stored procedures in a Fabric warehouse, and see where each fits in a repeatable transformation pipeline.
24. Level 24: Enriching Data with New Columns and Tables
    [Prepare data – Transform data] Learners add derived columns and lookup tables with Dataflows Gen2 custom columns, PySpark withColumn in a notebook, and T-SQL CTAS. They also learn when the enrichment belongs upstream rather than in the semantic model.
25. Level 25: Designing a Star Schema in Lakehouse or Warehouse
    [Prepare data – Transform data] Learners design fact and dimension tables with surrogate keys and a clear grain for a Fabric lakehouse or warehouse.
26. Level 26: Loading a Star Schema: Keys and Slowly Changing Dims
    [Prepare data – Transform data] Learners load dimensions and facts in the right order with T-SQL MERGE or Delta MERGE, look up surrogate keys, and handle Type 1 and Type 2 dimension changes.
27. Level 27: Denormalizing Data
    [Prepare data – Transform data] Learners flatten snowflaked or normalised sources into wide dimensions, and weigh fewer joins against redundancy and update cost.
28. Level 28: Aggregating Data into Summary Tables
    [Prepare data – Transform data] Learners build pre-aggregated tables with GROUP BY in T-SQL, Group By in Power Query and groupBy in PySpark, so large-volume queries and reports run faster.
29. Level 29: Merging and Joining Data
    [Prepare data – Transform data] Learners combine data with Power Query Merge and Append, SQL join types and UNION, and PySpark joins, and learn how each join type affects row counts.
30. Level 30: Resolving Duplicates, Missing Data and Nulls
    [Prepare data – Transform data] Learners detect and fix duplicate rows, missing records and null values with ROW_NUMBER, COALESCE, Power Query Remove Duplicates and Replace Values, and PySpark dropDuplicates and fillna.
31. Level 31: Converting Data Types and Filtering Data
    [Prepare data – Transform data] Learners convert column data types safely with CAST, TRY_CAST, Power Query type changes and PySpark cast, and filter rows early to cut data volume.
32. Level 32: Querying with the Visual Query Editor
    [Prepare data – Query and analyze data] Learners select, filter and aggregate warehouse or SQL analytics endpoint data with the Visual query editor's no-code diagram, and view the SQL it generates.
33. Level 33: Select, Filter and Aggregate with SQL
    [Prepare data – Query and analyze data] Learners write T-SQL against a warehouse or SQL analytics endpoint using WHERE, GROUP BY, HAVING, TOP and window functions to answer analytical questions.
34. Level 34: Select, Filter and Aggregate with KQL
    [Prepare data – Query and analyze data] Learners query an Eventhouse KQL database and KQL queryset with project, where, summarize, bin and render, and learn how KQL maps to SQL thinking.
35. Level 35: Select, Filter and Aggregate with DAX Queries
    [Prepare data – Query and analyze data] Learners write DAX queries in DAX query view with EVALUATE, SUMMARIZECOLUMNS, FILTER, TOPN and DEFINE MEASURE to query a semantic model directly.
36. Level 36: Choosing a Storage Mode
    [Implement and manage semantic models – Design and build semantic models] Learners compare the Import, DirectQuery, Direct Lake and Dual storage modes on latency, freshness, data volume and capability limits, and pick one for a scenario.
37. Level 37: Implementing a Star Schema in a Semantic Model
    [Implement and manage semantic models – Design and build semantic models] Learners model facts and dimensions in a semantic model with one-to-many single-direction relationships, a marked date table and role-playing dimensions.
38. Level 38: Bridge Tables and Many-to-Many Relationships
    [Implement and manage semantic models – Design and build semantic models] Learners resolve many-to-many scenarios with bridge (factless) tables or native many-to-many cardinality, and learn the trade-offs of bidirectional filtering.
39. Level 39: DAX Variables, Iterators and Table Filtering
    [Implement and manage semantic models – Design and build semantic models] Learners write measures with VAR/RETURN, iterators such as SUMX and AVERAGEX, and table filtering with CALCULATE, FILTER, KEEPFILTERS and ALL/REMOVEFILTERS.
40. Level 40: DAX Windowing and Information Functions
    [Implement and manage semantic models – Design and build semantic models] Learners use the window functions OFFSET, WINDOW, INDEX and RANK alongside information functions such as ISBLANK, HASONEVALUE, ISINSCOPE and SELECTEDVALUE to build period comparisons and context-aware measures.
41. Level 41: Calculation Groups, Dynamic Formats, Field Parameters
    [Implement and manage semantic models – Design and build semantic models] Covers the bullet 'Implement calculation groups, dynamic format strings, and field parameters': you build a time-intelligence calculation group with SELECTEDMEASURE() and calculation item format string expressions, add a dynamic format string to a measure, and create a field parameter so report users can swap the measures or dimensions shown in a visual.
42. Level 42: Large Semantic Model Storage Format
    [Implement and manage semantic models – Design and build semantic models] Covers the bullet 'Identify use cases for and configure large semantic model storage format': which scenarios need the large format (models growing past the default size limit, incremental refresh partitions, heavy XMLA write operations), how to turn it on in the semantic model settings or as a workspace default, and what capacity limits still apply.
43. Level 43: Designing and Building Composite Models
    [Implement and manage semantic models – Design and build semantic models] Covers the bullet 'Design and build composite models': mixing Import, DirectQuery and Dual storage modes in one model, chaining DirectQuery to a published Power BI semantic model or Analysis Services model, user-defined aggregation tables, and the limited relationships and security trade-offs these designs bring.
44. Level 44: Query and Report Visual Performance
    [Implement and manage semantic models – Optimize enterprise-scale semantic models] Covers the bullet 'Implement performance improvements in queries and report visuals': you use Performance analyzer to split a visual's time into DAX query, visual display and other, cut the number of visuals and slicers per page, use query reduction settings, and shrink the model (remove unused columns, lower cardinality, turn off auto date/time) so it scans faster.
45. Level 45: Improving DAX Performance
    [Implement and manage semantic models – Optimize enterprise-scale semantic models] Covers the bullet 'Improve DAX performance': how the formula engine and storage engine split the work, using variables so an expression is calculated only once, using KEEPFILTERS or plain column filters in CALCULATE instead of FILTER over whole tables, using DIVIDE, and reading Server Timings to confirm that a rewrite really helped.
46. Level 46: Direct Lake: Fallback, Refresh and OneLake vs SQL
    [Implement and manage semantic models – Optimize enterprise-scale semantic models] Covers the bullets 'Configure Direct Lake, including default fallback and refresh behavior' and 'Choose between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint': framing and automatic updates, the DirectLakeBehavior property (Automatic, DirectLakeOnly, DirectQueryOnly), what triggers DirectQuery fallback (including SQL-based security on the endpoint), and why Direct Lake on OneLake never falls back but works with OneLake security.
47. Level 47: Incremental Refresh for Semantic Models
    [Implement and manage semantic models – Optimize enterprise-scale semantic models] Covers the bullet 'Implement incremental refresh for semantic models': the reserved RangeStart and RangeEnd datetime parameters, filtering on them in Power Query so the filter folds to the source, setting the archive and incremental windows, detecting data changes, getting the latest data in real time with a DirectQuery partition, and managing the resulting partitions through the XMLA endpoint.
48. Level 48: Timed Mock Exam: DP-600 (50 Questions, 100 Minutes) Timed mock
    [All sections] A timed 50-question mock exam in 100 minutes, weighted like the October 19, 2026 skills outline: Maintain a data analytics solution 25–30%, Prepare data 45–50%, Implement and manage semantic models 25–30%. It mixes recognition, product-contrast and scenario 'best answer' questions with T-SQL, KQL and DAX, and the pass mark is 70%.

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