Databricks GenAI Engineer

Those figures are not stated in the material we could verify, so we report them as unknown rather than guessing. Databricks publishes the current item count, time limit, and delivery format in its official exam guide, which is revised periodically. Confirm there before you book a slot, and budget study time from the guide rather than from second-hand numbers.

We could not verify any of these from an authoritative source, so all four are recorded as null on this page. Fees, passing thresholds, waiting periods between attempts, and how long the credential stays valid are set by Databricks and change over time. Check the official certification page and its terms before scheduling.

The exam is understood to use multiple-choice items, which is the format Databricks typically uses for associate-level certifications. Expect scenario questions that describe a business problem and ask you to pick the most appropriate Databricks component, configuration, or evaluation approach rather than to recall isolated API signatures. Verify the current format in the official guide.

The platform fundamentals carry over, so the real lift is the GenAI layer: chunking and embedding strategy, Vector Search indexing and retrieval quality, prompt and agent design, MLflow evaluation, and serving. Candidates who have only read about RAG generally struggle. Building one working application end to end is usually the single biggest score improvement.

Start with RAG data preparation, because retrieval quality caps everything downstream. Then move to application design and build, followed by tuning and evaluation, since you cannot judge a pipeline you cannot measure. Deployment and monitoring come next, and governance and security last - though Unity Catalog permissions are easier to learn alongside deployment than in isolation.

Three recur: treating evaluation as an afterthought and being unable to describe how to measure retrieval or answer quality; confusing Vector Search, embeddings, and Foundation Model APIs when choosing a component; and ignoring governance until the end, so questions about permissions, PII handling, and auditability feel unfamiliar. Hands-on labs address all three more effectively than reading documentation.

No. Accelerators such as Trellis IQ, LakeSpeak, or the SAS-to-PySpark migration tools are partner-built commercial offerings marketed on the Databricks platform. They illustrate patterns - agentic code generation, automated data quality, natural-language interfaces - but they are ecosystem products, not exam blueprint content. Do not spend study time memorizing partner names or their claimed benchmarks.

Enough to build without copying a tutorial: create a Delta table, chunk and embed documents, index them in Vector Search, call a Foundation Model API, and log an evaluation run with MLflow. You should also be able to register a model or chain and expose it through a serving endpoint. Reading code is not sufficient preparation for scenario questions.

It is designed for exactly that profile. The exam assumes platform engineering competence - pipelines, storage, catalog, serving - and adds generative AI application concerns on top. Data engineers who already own Databricks workloads often find the governance and deployment domains easiest, while ML specialists may need more time on Unity Catalog and pipeline mechanics.