DP-700 · Microsoft
Studying for DP-700? What the October update means for you
DP-700 gets new objectives in October. How much actually changes, what the three skill areas weigh, and the objectives that ask you to pick one tool over another.
Microsoft DP-700 is the exam behind the Fabric Data Engineer Associate certification. The study guide describes someone who knows data loading patterns, data architectures and orchestration, and who ingests, transforms, secures, monitors and optimizes data in Microsoft Fabric. You are expected to work in SQL, PySpark and KQL, and to work alongside analytics engineers, architects, analysts and administrators.
What the exam measures
| Skill area | Weight |
|---|---|
| Implement and manage an analytics solution | 30 to 35% |
| Ingest and transform data | 30 to 35% |
| Monitor and optimize an analytics solution | 30 to 35% |
Three areas, each with a 30 to 35% weight range. There is no light section to leave for the end: monitoring and optimization have the same published range as ingestion.
Which version you will sit
- 21 July 2026: the version of the objectives the English exam measures until 19 October.
- 19 October 2026: the date Microsoft updates the English exam to a new version, already published in the study guide.
The October change is small. The change log rates every skill area as "no change" or "minor", the weights stay the same, and the only wording change is "Optimize Eventstreams and Eventhouses" becoming "Optimize Eventstream and Eventhouse". The details are in what actually moves on 19 October.
Localised versions usually follow the English one about eight weeks later.
How the exam is scored and delivered
- Time: 100 minutes.
- Scoring: Microsoft requires a score of 700 or greater.
- Languages: English, Japanese, Chinese (Simplified), German, French, Spanish and Portuguese (Brazil). If yours is not available, you can request an extra 30 minutes.
- Features: most questions cover generally available features; commonly used preview features can appear.
- Renewal: Microsoft associate certifications expire annually and are renewed through an online assessment on Microsoft Learn.
The choose-between objectives
The DP-700 study guide lists objectives that ask you to choose an option for a stated need:
- Transformation: Dataflows Gen2, notebooks, KQL or T-SQL.
- Orchestration: Dataflow Gen2, a pipeline or a notebook.
- Storage: an appropriate data store for the scenario.
- Streaming: an appropriate streaming engine; native tables or OneLake shortcuts in Real-Time Intelligence; standard OneLake shortcuts or query acceleration for OneLake shortcuts.
For each pair or trio, write down one situation where each option is the right answer. If you cannot, that is the gap.
Where else people slip
- Security at every level. Workspace and item access, then row-level, column-level, object-level and folder/file-level controls, dynamic data masking, sensitivity labels, endorsement, audit logs and OneLake security. It is a long list inside a single area.
- Messy data. Batch ingestion includes handling duplicate, missing and late-arriving data, not only the happy path.
- Workspace settings. Spark, domain, OneLake and Apache Airflow workspace settings are separate objectives.
- Troubleshooting by item. The guide lists errors to identify and resolve in pipelines, Dataflows, notebooks, eventhouses, eventstreams, T-SQL and OneLake shortcuts. Each has its own place to look.
A study plan that follows the guide
Each area has the same published range of 30 to 35%. Give all three areas study time, and use the study guide as a checklist.
- Implement and manage an analytics solution. Workspace settings; lifecycle management (version control, database projects, deployment pipelines); the security list above; orchestration with schedules, event-based triggers, parameters and dynamic expressions.
- Ingest and transform data. Full and incremental loads, preparing data for a dimensional model, OneLake shortcuts and mirroring, transforming with PySpark, SQL and KQL, and streaming with Eventstreams, Spark structured streaming and KQL, including windowing functions.
- Monitor and optimize an analytics solution. Monitoring ingestion, transformation and semantic model refresh, alerts, the troubleshooting list, and performance work on lakehouse tables, pipelines, data warehouses, eventstreams, eventhouses, Spark and queries.
Every date we track for this and the other exams we cover is in the exam calendar.