The short version
Take DP-750 if your work or your next job runs on Azure Databricks. Take DP-700 if it runs on Microsoft Fabric. If you do not know yet, pick the platform your employer or target employer already uses; the choice matters more than the order. Take both only when you truly work with both platforms, since each asks for hands-on knowledge that does not transfer automatically.
Both are associate-level Microsoft data engineering exams, both use a 700 passing score, and both credentials follow the same free yearly renewal. The content is where they split.
Side-by-side table
| Item | DP-750 | DP-700 |
|---|---|---|
| Full name | Implementing Data Engineering Solutions Using Azure Databricks | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Credential | Microsoft Certified: Azure Databricks Data Engineer Associate (per Microsoft’s announcement title) | Microsoft Certified: Fabric Data Engineer Associate |
| Level | Intermediate (associate credential) | Intermediate (associate credential) |
| Platform | Azure Databricks with Unity Catalog | Microsoft Fabric |
| Domains | Four: 15-20%, 15-20%, 30-35%, 30-35% | Three: 30-35% each |
| Languages and tools named | SQL, Python, Git, Microsoft Entra, Azure Data Factory, Azure Monitor | SQL, PySpark, KQL |
| Passing score | 700 of 1,000 | 700 of 1,000 |
| Time | 120 minutes on the certification page | 100 minutes on the credential page |
| Questions | Not published | Not published; 40 to 60 is typical |
| Proctored | Yes, through Pearson VUE | Yes, through Pearson VUE |
| Renewal | Free, yearly, open book | Free, yearly, open book |
| Outline version | Skills measured as of October 19, 2026 | Skills measured as of October 19, 2026 |
Microsoft’s certification pages list 10 languages for both exams, and DP-700’s page notes that interactive components may appear.
What each exam covers
DP-750 follows the life of data in Azure Databricks. The four domains are setting up the environment (compute and Unity Catalog objects), securing and governing Unity Catalog, preparing and processing data (modeling, ingestion, transformation, quality), and deploying and maintaining pipelines (jobs, Git, bundles, monitoring). The last two carry 30-35% each, so about two thirds of the exam is about building and running pipelines.
DP-700 follows an analytics solution in Fabric. Its three domains are implementing and managing the solution (workspace settings, lifecycle management, security, orchestration), ingesting and transforming data (loading patterns, batch, streaming), and monitoring and optimizing. Each is weighted 30-35%, so no area is a safe skip.
Where they overlap
- Data engineering thinking. Full versus incremental loads, slowly changing dimensions, streaming versus batch, and handling duplicates and late data appear in both worlds.
- Orchestration. Both ask when to use a pipeline, a notebook, or a scheduled job, and how to handle errors and retries.
- Security and governance. Both include access control at several levels, masking, and auditing.
- Monitoring and tuning. Both test how you find the cause of a slow or failing run.
- Spark. Both use Apache Spark, so Spark concepts such as skew and shuffle help in each.
Where they differ
- The product model. DP-750 centers on Unity Catalog objects and privileges. DP-700 centers on Fabric items such as workspaces, lakehouses, warehouses, and Eventhouse.
- Query languages. DP-700 names KQL, which DP-750 does not. DP-750 puts more weight on Python and SQL notebooks and on Git-based deployment.
- Streaming tools. DP-750 mentions Azure Event Hubs, Structured Streaming and Lakeflow pipelines. DP-700 mentions Eventstream and Real-Time Intelligence.
- Neighboring services. DP-750 expects some familiarity with Microsoft Entra, Azure Data Factory and Azure Monitor. DP-700 stays inside Fabric.
- Shape. DP-750’s two heavy domains put a lot of weight on build and run. DP-700 spreads weight evenly.
How to choose
- Look at your day job. Databricks workspace: DP-750. Fabric capacity: DP-700.
- Look at job ads you want. Count which platform appears in them; do not rely on one source.
- Look at your skills. If you already know Spark and Git, DP-750 may feel closer. If you know Power BI, SQL and pipelines, DP-700 may feel closer.
- Do not pick by difficulty. Both are hands-on exams. Judge readiness by scores in practice sets, not by which exam sounds easier.
If you move between employers who use different platforms, take the one you need now, then the second when you are using it.
Prerequisites and preparation
Neither exam lists a required earlier certification on the pages we checked. DP-750 expects experience with SQL, Python and software lifecycle practices. DP-700 expects skill with SQL, PySpark and KQL. See the DP-750 prerequisites and the DP-700 prerequisites for self-check lists.
More guides for DP-750
- DP-750 Exam Format, Scoring and Retakes
- DP-750 Study Plan: 4-Week and 8-Week
- DP-750 Domains Explained With Examples
- DP-750 Sample Questions With Answers
- Is DP-750 Hard? An Honest Difficulty Check
- DP-750 Renewal and Retakes Explained
- Is DP-750 Worth It? An Honest Look
- DP-750 Cheat Sheet: Key Facts to Review
- DP-750 vs Databricks Data Engineer Pro
Next step
Read the DP-750 overview or the DP-700 overview for the full outline of the one you chose. Practice sets are available for both: DP-750 practice tests and DP-700 practice tests.
FAQ
Is DP-750 the replacement for DP-700?
No. They cover different platforms, Azure Databricks and Microsoft Fabric, and each has its own study guide.
Can I take both exams?
Yes. Nothing on the pages we checked prevents it. Each needs separate preparation.
Which exam is harder?
It depends on your background. Compare your results in practice sets rather than trusting a general ranking.
Do both credentials expire?
Both are associate-level credentials, which Microsoft says expire annually and renew for free through an online assessment.