The dp 600 syllabus covers three domains published by Microsoft: Maintain a data analytics solution, Prepare data, and Implement and manage semantic models. Microsoft weights them by functional group rather than a single fixed number per domain, and each domain breaks into specific objective statements covering governance, data ingestion and transformation, and semantic model design and optimization.
The dp 600 syllabus is Microsoft's own outline for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric, the exam behind the Microsoft Certified: Fabric Analytics Engineer Associate credential. Microsoft groups the content into three domains and publishes a weighting for each one on the official study guide. This page lists every domain and the objective statements underneath it, taken from that guide, so you can see exactly what the exam draws on before you open a single practice question.
DP-600 exam objectives at a glance
| Domain | Weight |
|---|---|
| Maintain a data analytics solution | 28% |
| Prepare data | 46% |
| Implement and manage semantic models | 26% |
Microsoft describes these as functional groups rather than a strict per-question split, so treat the weighting as a guide to where the bulk of the exam sits rather than an exact question count per domain. Microsoft does not publish how many questions the exam contains, only how the content is distributed across the three groups.
Maintain a data analytics solution
This domain covers security, governance, and the lifecycle tooling around a Fabric analytics solution. The published objectives are:
- Implement security and governance: workspace-level access controls, item-level access controls, row-level, column-level, object-level, and file-level access control, applying sensitivity labels to items, and endorsing items.
- Maintain the analytics development lifecycle: configuring version control for a workspace, creating and managing a Power BI Desktop project (.pbip), creating and configuring deployment pipelines, performing impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models, deploying and managing semantic models by using the XMLA endpoint, and creating and updating reusable assets such as Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models.
In practice, this domain asks whether you can lock down who can see and edit what, and whether you can move a solution through workspaces without losing lineage or breaking downstream reports.
Prepare data
This is the largest domain by weighting, and it covers getting data into Fabric and shaping it once it is there. The published objectives are:
- Get data: creating a data connection, discovering data by using the OneLake catalog and Real-Time hub, ingesting or accessing data as needed, choosing between different data stores, and implementing OneLake integration for Eventhouse and semantic models.
- Transform data: creating views, functions, and stored procedures, enriching data by adding new columns or tables, implementing a star schema for a lakehouse or warehouse, denormalizing data, aggregating data, merging or joining data, identifying and resolving duplicate data, missing data, or null values, converting column data types, and filtering data.
Expect scenario questions that ask you to choose the right ingestion pattern or transformation approach for a stated constraint, such as latency, cost, or a source system that cannot be duplicated.
Implement and manage semantic models
This domain covers building and tuning the semantic models that reports run against. The published objectives are:
- Design and build semantic models: choosing a storage mode, implementing a star schema for a semantic model, implementing relationships such as bridge tables and many-to-many relationships, writing DAX calculations that use variables and functions such as iterators, table filtering, windowing, and information functions, implementing calculation groups, dynamic format strings, and field parameters, identifying use cases for and configuring large semantic model storage format, and designing and building composite models.
- Optimize enterprise-scale semantic models: implementing performance improvements in queries and report visuals, improving DAX performance, configuring Direct Lake including default fallback and refresh behavior, choosing between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint, and implementing incremental refresh for semantic models.
This domain leans on DAX fluency and an understanding of the storage-mode trade-offs between Import, DirectQuery, and Direct Lake.
How the syllabus maps to preparation
Once you know the three domains and their weighting, the natural next step is a plan that spends time in proportion to that weighting rather than evenly across topics. Our DP-600 study plan builds a week-by-week schedule around this exact breakdown. If you want to check your prerequisites before committing to a schedule, see DP-600 prerequisites, and for the mechanics of the test itself, see exam format and scoring.
Where to go from here
This objectives list is the map; the rest of the hub covers the territory. Start with the DP-600 overview if you have not decided whether this exam fits your role, check cost and booking once you are ready to schedule, and work through sample questions to see how these objectives turn into exam-style items.
Exam facts on this page come from Microsoft’s own pages (official exam page). Prices, versions and policies change — confirm there before you book.