DP-750 vs DP-700: Databricks or Fabric?
Take DP-750 if your pipelines run on Azure Databricks, and DP-700 if they run on Microsoft Fabric. Both are Microsoft associate-level data engineering exams with a passing score of 700, so neither is an easier shortcut to the other. They test different platforms, and the platform you work on should decide the choice.
The two exams side by side
| DP-750 | DP-700 | |
|---|---|---|
| Exam title | Implementing Data Engineering Solutions Using Azure Databricks | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Certification | Azure Databricks Data Engineer Associate | Fabric Data Engineer Associate |
| Platform | Azure Databricks: Spark clusters and serverless compute, Unity Catalog, Lakeflow | Microsoft Fabric: lakehouses, warehouses, pipelines, eventhouses |
| Languages named in the role | SQL and Python | SQL, PySpark and KQL |
| Passing score | 700 / 1000 | 700 / 1000 |
| Duration | 120 minutes | See the DP-700 section |
| Skill areas | Four | Three |
What the skill areas look like
DP-750 splits into setting up the environment, securing and governing Unity Catalog, preparing and processing data, and deploying and maintaining pipelines. Compute selection, Unity Catalog privileges, Spark Structured Streaming, Auto Loader, Lakeflow Jobs and bundle deployment all belong to Databricks.
DP-700 is organised around ingesting and transforming data, securing and managing an analytics solution, and monitoring and optimising it, in a Fabric workspace. Pipelines, dataflows, notebooks, KQL and eventstreams are Fabric items with no direct counterpart in the DP-750 outline.
Where they overlap
The ideas are the same even though the products differ: batch and streaming ingestion, Delta tables, incremental loads, slowly changing dimensions, partitioning, orchestration, access control and monitoring. Someone who has passed one will find the concepts in the other familiar, but not the settings, the names of features or the operational details.
Fabric stores data in OneLake in Delta format, and Databricks works naturally with Delta tables, so the table-format knowledge transfers well. What does not transfer is how you provision compute, who governs access, and how jobs are orchestrated.
Which one should you take?
- Your team runs Databricks on Azure: DP-750. It is also the better signal if you use Unity Catalog, Lakeflow and Spark day to day.
- Your organisation has standardised on Fabric and Power BI: DP-700.
- You want Databricks credentials from the vendor itself: the Databricks Data Engineer Associate is a separate exam run by Databricks, with a different format and little Azure-specific content. See the comparison of Databricks and Azure data engineering routes for the trade-offs.
- You do both: common for consultants. Start with the platform you use at work.
DP-750 and the Databricks associate
The Microsoft exam expects Azure knowledge that the Databricks exam does not: Microsoft Entra service principals and managed identities, Azure Key Vault secrets, Azure Data Factory as an ingestion option, Event Hubs as a streaming source, and Azure Monitor for logs and alerts. The Databricks exam, in turn, is tied to the vendor’s own outline. If you have already passed the Databricks associate, expect the Spark, Delta and Unity Catalog parts of DP-750 to feel familiar and the Azure integration parts to need study.
Effort
Plan about four weeks for either exam with relevant hands-on experience (see the study plan), and longer if you are new to the platform. Without Spark or Databricks experience, DP-750 is the harder start, because you are learning a distributed engine, a governance model and a deployment workflow at the same time.
A sensible order
- Decide by the platform you work on, not by which exam sounds newer.
- Read the exam format and the prerequisites to check you meet the entry level.
- Follow the study plan and take the practice test.