The Databricks Certified Data Engineer Associate is an entry-level credential that validates your ability to build and maintain data pipelines on the Databricks Data Intelligence Platform. It is a 45-question, 90-minute proctored exam covering ingestion, transformation, production pipelines, and governance. This guide covers the current 2026 format, what's on it, and how to prepare.
Last updated July 2026 · reflects the May 2026 exam version (7 sections).
The Databricks Certified Data Engineer Associate is an entry-level certification that proves you can perform foundational data engineering tasks on the Databricks Data Intelligence Platform — ingesting data, transforming it with Spark SQL and PySpark, orchestrating production pipelines with Lakeflow Jobs, and applying governance with Unity Catalog.
It is the first rung on the Databricks data engineering ladder (the next step up is the Data Engineer Professional). Passing earns you a verifiable digital badge that is valid for two years. The exam is written for practitioners, so questions are scenario-based and reward understanding why one tool or pattern beats another, not just memorised syntax.
There are no formal prerequisites — anyone can sit the exam. In practice it fits people who already touch data and want the Databricks stack on their résumé:
Databricks recommends roughly six months of hands-on experience with the platform, but that's a guideline, not a gate. If you understand the medallion architecture, can read PySpark and SQL, and have loaded data into a Delta table, you have the right foundation to start studying.
Because Databricks periodically updates the exam, always confirm the latest details in the official exam guide before you book. As of the May 2026 version:
| Questions | 45 multiple-choice questions |
|---|---|
| Time limit | 90 minutes |
| Cost | $200 USD (plus local tax) |
| Passing score | Not officially published; ~70% is the widely cited threshold |
| Delivery | Online, proctored |
| Prerequisites | None required (≈6 months' hands-on experience recommended) |
| Validity | Valid for 2 years; recertify to stay current |
| Language | English |
Figures reflect the May 2026 exam version and can change — verify against the official guide when you register.
The May 2026 revision restructured the exam from five domains into seven sections, adding dedicated coverage of Lakeflow Jobs, CI/CD, and troubleshooting. Our course is organised around exactly these seven units:
The lakehouse architecture, Delta Lake fundamentals (ACID transactions, time travel), Unity Catalog, and the compute and cost models. This section establishes the mental model everything else builds on.
Getting data into the lakehouse: batch, streaming, and incremental patterns; COPY INTO; Auto Loader with schema enforcement and evolution; Lakeflow Connect; and — crucially — knowing which method to reach for in a given scenario.
ELT with Spark SQL and PySpark: Bronze→Silver cleaning, joins, column and row manipulation, deduplication and aggregations, Gold-layer objects (views, materialized views, streaming tables), and building data-quality checks into the flow.
Orchestrating production pipelines: the anatomy of a job and its task DAG, control flow (retries, branching, looping), trigger types, and the trade-off between time-based and data-driven scheduling.
Shipping pipelines like software: Databricks Git Folders, automation with Asset Bundles, promotion across dev → test → prod environments, and the Databricks CLI.
Keeping pipelines healthy: reading job health, spotting Spark bottlenecks (skew, shuffle, spill) in the Spark UI, Liquid Clustering and predictive optimization, diagnosing cluster failures, and tracking performance against baselines.
Locking data down with Unity Catalog: managed vs. external tables, access control with GRANT/REVOKE/DENY, column masking and row-level security, and attribute-based access control (ABAC) policies.
The exam rewards reasoning, not recall, so cramming facts doesn't get you far. The most reliable way through is a tight Learn → Recall → Reason loop on each concept: learn it, pull it back from memory, then apply it to a scenario where two options both look right.
Short, visual chapters that explain a single idea — the lakehouse, Auto Loader, ABAC — in a couple of minutes, without the wall of docs.
Active recall locks the idea in before you move on, so it's still there on exam day instead of a week later.
Scenario questions force you to choose between close options — Auto Loader vs. COPY INTO, time-based vs. data-driven triggers — which is exactly how the real exam tests you.
That's how this course is built: 35 chapters across the seven exam sections, each followed by practice exams with full explanations, so you close every gap before test day.
It's considered moderate — approachable for anyone with real Databricks experience, but not a giveaway. The difficulty comes from scenario questions that ask you to choose the best tool or pattern when several options are plausible, rather than from obscure trivia. Candidates who study the concepts and practise with realistic questions generally pass on the first attempt.
Most candidates need two to four weeks of focused study. If you already work with Spark and Delta Lake daily, one to two weeks of review is often enough. If the platform is newer to you, budget four to six weeks and lean on hands-on practice and full-length practice exams.
Yes. The exam is 45 multiple-choice questions delivered online with a proctor, to be completed within 90 minutes. There are no hands-on coding tasks — but many questions show code or a scenario and ask you to pick the correct or best answer.
Yes. The Databricks Certified Data Engineer Associate credential is valid for two years. To stay certified you retake the current version of the exam before it lapses, which keeps your certification aligned with platform updates.
The $200 fee, the 45-question format, and step-by-step booking and proctoring.
2-, 4-, and 6-week study plans built from the 35 chapters, by experience level.
How the two data engineering certs differ, and which one to take first.
Bronze, Silver, and Gold layers — one of the most-tested concepts on the exam.
Why "DLT" is now Lakeflow Declarative Pipelines, and what it means for the exam.
Begin with the lakehouse — the concept everything else on the exam builds on. Two minutes, and it sticks.
Start Chapter 1 →