Databricks Generative AI Engineer Associate exam guide
The Databricks Certified Generative AI Engineer Associate is a 90-minute, US$200 proctored exam for people who build RAG and agent applications on Databricks, and it tests six domains from design to evaluation. Take it once you have built something hands-on in Databricks; the free Generative AI Fundamentals badge is a useful warm-up, not a prerequisite or a replacement.
This guide explains the current format, price, registration rules, six domains, and a practical way to prepare. It also separates the current blueprint from older material, explains what to do with an unpublished passing score, and points to original practice questions that exercise the same decisions without claiming to reproduce the exam.
Databricks Generative AI Engineer Associate exam at a glance
Databricks calls the certification Databricks Certified Generative AI Engineer Associate. It is live as of October 2026, with no beta or retirement notice in the current materials, and is aimed at engineers who design and implement LLM-enabled solutions, including RAG applications and LLM chains. Unlike AWS or Microsoft exams, it has no short public exam code.
The exam format, as of October 2026, is 90 minutes with 45 scored questions and up to 10 unscored questions. The current guide describes multiple-choice and multiple-selection items and does not allow aids, and the certification page says any machine-learning code is in Python. Treat the extra unscored items as part of the sitting rather than assuming every delivered item affects your result.
- Prerequisites: none are required, although related training and six months of hands-on Databricks experience are recommended.
- Delivery: the certification page lists online or test-centre delivery, while the exam guide describes online proctoring. Confirm the available option when you register.
- Passing result: Databricks does not publish a numeric passing score for this certification.
The current certification page is the place to check assessment details before booking. For the scope behind those details, use the August 2026 exam guide, which identifies the version that is live.
Who should take it, and which Databricks credential to start with
This certification fits a data, ML, or application engineer who wants to build LLM applications on Databricks rather than only describe generative AI. It rewards the ability to make connected engineering choices: how an application is designed, how retrieval data is prepared, how it is deployed, and how it is governed and evaluated. It is not presented as a formal prerequisite for another Databricks certification.
If you already understand LLM application patterns but are new to Databricks, begin with the free Databricks Generative AI Fundamentals badge and then build in the Free Edition before deciding you are ready for the associate exam. If you know Databricks but are new to generative AI, use the badge as orientation, then put more time into RAG, agents, evaluation, and deployment work. Neither starting point removes the value of hands-on practice against the six domains.
RAG is central because the work joins source data, retrieval, an LLM response, and evaluation. Read what retrieval-augmented generation is if that workflow is new, then compare it with model adaptation in the RAG versus fine-tuning guide. Those guides provide the concepts; the certification expects you to apply them in Databricks choices.
Price, registration and retakes
The exam price is US$200 plus applicable local taxes as of October 2026. Databricks does not publish an INR price, so candidates in India should check the final currency conversion, taxes, and payment total during registration rather than rely on a converted figure from another site. This is a per-attempt fee, not the price of training.
Register through Databricks on Webassessor, the Kryterion registration route. If you need another attempt, the policy permits a retake after 14 days, allows unlimited attempts, and charges the full fee for each attempt. There are no free retakes listed in the current policy, so set your appointment after you have completed the practice and review you need.
The paid instructor-led Generative AI Engineering with Databricks course has a public-class price of US$1,500 as of October 2026. That is optional preparation, not an exam requirement; its course page is useful for checking current offerings before treating its price as part of your plan.
What the exam tests: the domains
The domain weights describe a full application lifecycle, not six isolated vocabulary lists. Application Development and Assembling and Deploying Applications together account for more of the blueprint than the other areas, but a weak retrieval, governance, or evaluation decision can still undermine an otherwise capable application. Organise practice around the choices a builder has to make at each stage.
Design Applications (14%)
This domain tests whether you can choose an application approach that matches a user's job and the information it needs. For a question-answering experience over documents, the important work is deciding what kind of agent or application suits the outcome, rather than treating every AI feature as interchangeable. Be ready to connect a requirement such as cited answers to the appropriate design choice.
Start with a practice question on selecting an Agent Bricks option. It asks you to distinguish a Knowledge Assistant use case from other Agent Bricks choices, which is the kind of product-to-requirement reasoning this domain measures.
Data Preparation (14%)
Data Preparation tests whether material will remain useful after it is turned into retrievable content. You should be able to reason about chunking, overlap, embeddings, and the limit imposed by the embedding model, rather than assuming that putting a document in an index makes all of its text available. That distinction matters when an answer depends on information near the end of a long source section.
Use a practice question on chunk size and an embedding limit to test that reasoning. The scenario shows why content beyond a 512-token embedding limit can be lost to retrieval and why changing the chunks, with overlap where appropriate, is a preparation decision rather than a prompt fix.
Application Development (30%)
Application Development is the largest domain at 30%, so give it the largest share of your hands-on study. It tests the work of building the LLM-enabled application, including retrieval and the choices that make its search behaviour fit a real request. Learn the current name AI Search alongside the older term Vector Search so that a change in vocabulary does not hide a familiar capability.
For an example of a retrieval decision, try a practice question on hybrid search for part numbers. It explores why a request that combines ordinary language with an exact identifier needs a hybrid query rather than a choice based only on one kind of matching.
Assembling and Deploying Applications (22%)
This domain tests how you package, promote, deploy, and operate an application after its core logic exists. Treat a prompt and its version as deployable assets: changing an asset safely is different from making an untracked edit in a running application. You should be able to explain how a release can move forward and roll back without redeploying the whole service.
Work through a practice question on Prompt Registry aliases. It focuses on immutable versions and a production alias, helping you connect version control and promotion to an application deployment workflow.
Governance (8%)
Governance tests whether you can apply controls around an AI application's data and behaviour. Although it is the smallest weighted domain at 8%, it is where you connect a policy goal, such as handling personally identifiable information, to the control's behaviour and limitations. Know the difference between allowing a conversation to continue with protected content changed and stopping it altogether.
Try a practice question on AI Gateway PII guardrails. It makes you distinguish Mask from Block and consider where the guardrail applies, a concrete way to practise a governance choice rather than memorising a product label.
Evaluation and Monitoring (12%)
Evaluation and Monitoring tests whether you can tell if an application is doing useful, grounded work after it is built. That means selecting an evaluation approach based on what evidence is available, reading the signal it produces, and using monitoring to investigate a problem instead of assuming a fluent answer is a correct one. Give this domain its own practice time rather than leaving it for a final review.
Use a practice question on judges that require ground truth. It asks you to identify when an evaluation input needs expected results, which is a useful check that you understand what a judge can and cannot establish from a trace alone.
Is your study material out of date?
Use the current exam guide as the starting point for syllabus decisions. It is titled August 2026, and its update table gives 18 March 2026; older community material can describe a prior version without making that distinction, so check the date on anything you study from.
One naming change does matter. Vector Search became AI Search on 1 June 2026: the current exam guide still uses the earlier term, while the certification page uses AI Search. Learn both names when reading resources, but check the current scope before spending time on an old tutorial or assuming a changed product label represents a different topic.
Be cautious with material that turns an unpublished cut score into a certainty. Databricks says its certification scores are set statistically and can change as exams are updated, so a fixed target from an older page is not a reliable readiness rule. Prefer current official objectives, the sample questions in the guide, and your ability to explain each engineering decision.
How to prepare: free Databricks Academy courses and practice
Start with free official material: the exam guide includes 10 sample questions and an answer key, and Databricks also offers an AI Prep Guide, Databricks Free Edition, and product documentation. The free Generative AI Fundamentals badge is a sensible first pass for an introductory audience. Databricks does not offer an official practice exam for this certification, so use its material to define scope rather than wait for a vendor mock test.
For domain practice, the official catalog lists Building RAG Agents with Agent Bricks, Building Agentic Applications on Databricks, Agent Evaluation on Databricks, and Deploying and Monitoring Agent Applications on Databricks. The public self-paced course pages do not state a confirmed price, so check each course before assuming its availability or cost.
Use the Databricks Generative AI Engineer Associate practice tests and the complete Databricks Generative AI Engineer Associate question list after you have studied the matching objective. These original questions are most valuable for finding a decision you cannot yet explain, then returning to the course, guide, or Free Edition to test that decision in context. Do not use a score from this smaller practice set as if it were an official prediction of your exam result.
For a broader decision about where this credential sits, compare the paths in the best AI certifications guide and the best MLOps certifications guide. They help you decide whether you want a Databricks generative AI application credential or a different AI or MLOps focus; they do not replace preparation for this specific blueprint.
How to know you're ready when the passing score isn't published
Do not try to reverse-engineer a guaranteed result from a public percentage. Databricks does not publish the passing score and says certification scores are set through statistical analysis and can change when an exam changes. The useful question is whether you can make and justify the engineering decisions the blueprint names under a timed, multiple-choice format.
Use the domain weights to set review priority, then test the gaps your answers reveal. You should be able to select an application design, explain how source content is prepared for retrieval, choose a search approach, describe a controlled promotion, apply a privacy control, and choose an evaluation input that fits the claim being assessed. When you miss one of those decisions, do the underlying exercise in Free Edition and explain why the other choices would not meet the need.
Complete the current guide's sample questions only after you have worked through the objectives, then review each explanation even when your answer was right. A readiness check is stronger when you can connect the explanation to work you have actually performed, rather than recognise a phrase from a course. Leave time to revise the domain where your reasoning is least consistent.
Validity and renewal
The certification is valid for two years as of October 2026. To recertify, take the current live version of the exam; Databricks does not list a shorter renewal assessment. Because the current version may change before renewal, review the then-current guide rather than assume today's product names and objectives will remain unchanged.
Keep your preparation notes organised by the six domains, especially the decisions you made in your own applications. That makes it easier to find which parts of a future blueprint have moved and which underlying skills remain useful. If your role changes toward a wider MLOps remit, the related certification comparisons can help you reassess the next credential rather than treating renewal as the only option.
Frequently asked questions
How many questions are on the exam?
As of October 2026, the guide lists 45 scored questions plus up to 10 unscored questions in a 90-minute session. The questions are multiple-choice and multiple-selection. Pace for the whole delivered session instead of assuming that every item has the same scoring role.
What is the passing score?
Databricks does not publish a numeric passing score for this certification. It says certification scores are set statistically and may change as the exams are updated. Focus on the current objectives and your ability to justify each choice instead of treating an old public target as a requirement.
How much does it cost in India?
The listed price is US$200 plus applicable local taxes as of October 2026. Databricks does not publish an INR price, so the amount you pay in India depends on the registration total and any currency conversion or payment charges. Check the amount before confirming your appointment.
Do I need Databricks experience?
There is no formal prerequisite, but Databricks recommends six months of hands-on experience. If you have less experience, use the free Fundamentals badge, Free Edition, and the official courses to build application, retrieval, deployment, governance, and evaluation practice before booking. General LLM knowledge helps, but it does not by itself cover the Databricks workflow.
Is Generative AI Fundamentals required?
No. Databricks Generative AI Fundamentals is a free introductory badge for technical and nontechnical professionals, while the Associate certification is a separate paid, proctored exam. Use the badge if you need a structured introduction, then decide whether your hands-on work is ready for the associate-level scope.
How long should I study for the exam?
Databricks does not publish a study-time estimate for this certification. Plan from your starting point: someone new to Databricks needs time to build hands-on experience, while a practitioner can concentrate on the current objectives and weak domains. Book after you can consistently explain and apply the six domain decisions, not after a fixed number of study hours.