Databricks Certified Generative AI Engineer Associate Practice Exam
Practice across all six exam sections — from designing LLM applications and preparing data to deploying, governing, and monitoring production GenAI on Databricks. Get immediate feedback in Learn mode and a full 90-minute simulation in Exam mode. Start with a 24-hour free trial.
Start 24-hour free trial →Exam at a glance
- Exam
- Databricks Certified Generative AI Engineer Associate
- Format
- Multiple choice, proctored (online or test center)
- Scored questions
- 45 (additional unscored items may appear)
- Time limit
- 90 minutes
- Registration fee
- $200 USD, plus applicable local taxes
- Prerequisites
- None; related training highly recommended
- Recommended experience
- 6+ months of hands-on GenAI solution work on Databricks
- Passing standard
- Databricks does not publish a fixed numeric passing score
- Validity
- 2 years; recertify by taking the current exam
- Languages
- English, Japanese, Portuguese (BR), Korean
- Blueprint edition
- Exam Guide, March 2026 edition
Source: Databricks — Certified Generative AI Engineer Associate · Exam Guide PDF (Mar 2026)
About this certification
The Generative AI Engineer Associate is Databricks’ credential for engineers who build and ship LLM-enabled applications rather than just experiment with models. It validates that you can decompose a business problem into model tasks, choose appropriate models and tools from the current GenAI landscape, and assemble a working application end to end — most commonly a retrieval-augmented generation (RAG) pipeline or an LLM chain that runs on Databricks. Where many AI certifications stop at concepts, this one is explicitly engineering-focused: it expects you to know the Databricks toolchain that makes those applications production-grade — Vector Search for semantic retrieval, Model Serving for deployment, MLflow for lifecycle management, and Unity Catalog for governance.
Because the exam mirrors a real delivery lifecycle, the heaviest-weighted material sits in the middle of that lifecycle: building the application and getting it deployed. The lighter-weighted sections — governance and evaluation/monitoring — are not afterthoughts but reflect how much exam surface area Databricks allocates to each stage. All code shown in the exam is Python, with some non-ML data manipulation possibly shown in SQL. For foundational reading on building production GenAI systems, see the Generative AI Engineering Learning Hub guide.
Exam sections and weights
The exam is divided into six sections. Weights are taken directly from the Databricks Exam Guide (March 2026 edition); approximate question counts are derived from the 45 scored questions and rounded.
Translate business requirements into model tasks; decompose problems; choose prompts, models, and chain components.
Chunk and prepare source documents; build the data layer that feeds retrieval, including how chunks are stored for Vector Search.
The largest section. Build LLM chains and RAG applications, engineer prompts, integrate tools, and compose multi-stage pipelines.
Log models with MLflow, register to Unity Catalog, and deploy to Model Serving endpoints — including Foundation Model APIs.
Apply guardrails, masking, licensing checks, and Unity Catalog controls so applications meet legal and safety requirements.
Evaluate LLM output with metrics and LLM-as-a-judge; monitor deployed apps with inference tables and MLflow.
Who this exam is for
This credential fits engineers and practitioners who are already building, or moving into building, GenAI applications on the Databricks platform — including roles such as generative AI engineer, machine learning engineer, AI/ML developer, and data engineers expanding into LLM work. There are no formal prerequisites, so anyone can register; in practice Databricks recommends around six months of hands-on experience with GenAI solution tasks, working Python proficiency, comfort with a framework such as LangChain, and familiarity with the core Databricks tools (MLflow, Unity Catalog, Vector Search, Model Serving).
If you are newer to the platform, the Data Engineer Associate or Machine Learning Associate paths build foundational Databricks skills that make this exam more approachable. For role-by-role salary ranges and career paths, see the Career Hub — AI & Machine Learning Engineer role guide.
What this practice exam delivers
Learn mode
Answer one question at a time with the explanation revealed immediately — ideal for the Application Development section, where the right architectural choice is rarely obvious on first read.
Exam mode
45 questions against a 90-minute timer — the real exam format. Build the pacing the scenario-based questions demand before test day.
Source-linked explanations
Every answer cites the Databricks documentation it derives from — MLflow, Vector Search, Model Serving, Unity Catalog — so you can verify the reasoning and dig deeper.
Score by exam section
Results break down across all six sections — Design, Data Preparation, Development, Deployment, Governance, and Evaluation — so practice tells you exactly which stage of the lifecycle to study next.
Sample practice questions
Ten free questions spanning the six exam sections, each with a full explanation of why the other answers are wrong. The complete bank is available with the 24-hour trial.
A Generative AI Engineer must build an application that needs a foundation LLM with a large context window for long-document reasoning. Which model best fits this need?
- DistilBERT
- BGE-large
- Llama 2 70B
- A small fine-tuned classifier
Show answer & explanation
Correct: C — Llama 2 70B. A large general-purpose generative LLM such as Llama 2 70B offers the broad reasoning ability and longer context window needed for long-document tasks, and is available through Databricks Foundation Model APIs.
Why not the others: DistilBERT (A) is a small encoder model for classification, not generation; BGE-large (B) is an embedding model used for retrieval, not text generation; a small fine-tuned classifier (D) cannot generate free-form reasoning over long context. Matching model type to task is the core Design Applications skill.
Source: Databricks — Foundation Model APIs →An engineer is decomposing a customer-service automation project. Which approach best reflects sound problem decomposition for a GenAI solution?
- Use a single prompt to one general LLM for every request
- Break the objective into sub-tasks (intent detection, retrieval, response generation) and map each to an appropriate model or tool
- Fine-tune one model on all historical tickets and route everything to it
- Build a rules engine and avoid LLMs entirely
Show answer & explanation
Correct: B. Effective decomposition breaks a complex objective into manageable sub-tasks and matches each to the right model, tool, or chain step — the explicit goal of the Design Applications section.
Why not the others: a single catch-all prompt (A) ignores task-specific accuracy and cost trade-offs; fine-tuning one model on everything (C) is expensive and brittle versus composing specialized steps; abandoning LLMs (D) discards the capability the project requires. Decomposition then orchestration is the pattern.
Source: Databricks — Build a GenAI app → Further reading: PowerKram — Designing LLM Applications →An engineer has chunked documents into a dataframe with two columns: the document file name and an array of text chunks per document. What is the most performant way to store this for a Vector Search index?
- Split into train/test sets, add a document ID, and save to a Delta table
- Flatten to one chunk per row, add a unique ID per row, and save to a Delta table
- Add a unique ID per document and save the array column directly to a Delta table
- Store each chunk as a separate JSON file in a Unity Catalog Volume
Show answer & explanation
Correct: B. Vector Search indexes one embedding per row, so the dataframe should be flattened to one chunk per row with a unique identifier, then saved to a Delta table that the index syncs from.
Why not the others: train/test splitting (A) is irrelevant to indexing and loses data; keeping arrays per document (C) leaves multiple chunks per row, which the index cannot embed cleanly; individual JSON files (D) bypass the Delta-backed sync that Vector Search expects and scale poorly.
Source: Databricks — Vector Search → Further reading: PowerKram — Chunking & RAG Data Prep →A RAG application must extract text from source PDFs that contain both text and images, using the fewest lines of code. Which Python package is the best fit?
- beautifulsoup
- unstructured
- requests
- numpy
Show answer & explanation
Correct: B — unstructured. The unstructured library is purpose-built to extract and partition text from mixed-content documents like PDFs with minimal code, which is why it is a common choice in Databricks RAG data-prep workflows.
Why not the others: beautifulsoup (A) parses HTML/XML, not PDFs; requests (C) only fetches content over HTTP; numpy (D) is for numerical arrays, not document parsing. Choosing the right ingestion library is a Data Preparation skill.
Source: Databricks — RAG data pipeline →An engineer is building a multi-stage reasoning pipeline that retrieves documents, summarizes them, and then generates a response. Which framework is most directly suited to composing these chain components?
- LangChain
- Matplotlib
- Apache Kafka
- scikit-learn
Show answer & explanation
Correct: A — LangChain. LangChain is designed to compose multi-stage LLM chains — chaining retrieval, summarization, and generation steps — and integrates with Databricks tools, which is why the exam treats it as a core development framework.
Why not the others: Matplotlib (B) is a plotting library; Kafka (C) is a streaming message broker, not an LLM orchestration tool; scikit-learn (D) is classical ML and does not compose LLM chains. Building chains is the heart of Application Development.
Source: Databricks — LangChain on Databricks → Further reading: PowerKram — Building LLM Chains & Agents →A chatbot must classify the type of question a user asks and route it to the most appropriate model. Which design pattern does this describe?
- Fine-tuning a single monolithic model
- A routing or multi-model orchestration pattern, where an upstream step directs each query to a specialized model
- Caching every response in a Delta table
- Increasing the context window of one model
Show answer & explanation
Correct: B. Detecting the query type and routing to a specialized model is a routing/orchestration pattern — a common multi-stage design tested in Application Development.
Why not the others: a single monolithic model (A) defeats the purpose of routing; caching (C) addresses latency, not classification and routing; enlarging context (D) does not decide which model should answer. The defining behavior is classification-then-route.
Source: Databricks — GenAI app patterns →An LLM has been trained on Databricks and is ready to deploy. Which sequence is the easiest, recommended deployment process?
- Log the model as a pickle, upload to a Unity Catalog Volume, register with MLflow, then start a serving endpoint
- Log the model with MLflow during training, register it directly to Unity Catalog via the MLflow API, then start a serving endpoint
- Save the model locally, build a Docker image, and run a container
- Wrap the prediction function in a Flask app served by Gunicorn
Show answer & explanation
Correct: B. The Databricks-native path is to log the model with MLflow, register it to Unity Catalog through the MLflow API, and serve it from a Model Serving endpoint — the integrated, lowest-friction workflow.
Why not the others: pickling and manually uploading to a Volume (A) is more steps and skips MLflow lineage; building Docker images (C) and a Flask/Gunicorn app (D) are manual, non-native approaches that bypass Model Serving and Unity Catalog governance.
Source: Databricks — Model Serving → Further reading: PowerKram — Deploying Models with MLflow →An application was built using a provisioned-throughput Foundation Model API endpoint, but its request volume is too low to justify a dedicated provisioned endpoint. What is the more cost-appropriate option?
- Keep the provisioned-throughput endpoint regardless of volume
- Switch to a pay-per-token Foundation Model API endpoint that bills per request
- Retrain the model to be smaller
- Move the model to a local laptop
Show answer & explanation
Correct: B. For low or variable volume, pay-per-token Foundation Model APIs bill per request and avoid the fixed cost of a provisioned-throughput endpoint, which is the right deployment economics for this workload.
Why not the others: keeping provisioned throughput (A) wastes reserved capacity at low volume; retraining smaller (C) does not address the billing model and risks quality; a local laptop (D) is not a production serving option and abandons governance and scaling. Matching the serving mode to traffic is the deployment skill being tested.
Source: Databricks — Foundation Model API modes →A team must prevent prompt-injection attacks and block inappropriate content from an external-facing chatbot. Which combination of techniques best addresses this?
- Increase the model temperature
- Input validation, prompt sanitization, and content-filtering guardrails (e.g., a safety model such as Llama Guard)
- Disable logging to reduce data exposure
- Use a larger embedding model
Show answer & explanation
Correct: B. Guardrails — input validation, prompt sanitization, and context-aware content filtering, often backed by a safety model like Llama Guard — are the governance controls that defend against prompt injection and unsafe output.
Why not the others: raising temperature (A) makes output less predictable, not safer; disabling logging (C) harms monitoring and auditability without stopping attacks; a larger embedding model (D) improves retrieval, not safety. Guardrails are squarely a Governance topic.
Source: Databricks — AI guardrails → Further reading: PowerKram — Guardrails & Responsible AI →An engineer needs to evaluate the quality of free-form LLM responses at scale, where no single reference answer exists. Which approach is most appropriate?
- Exact string match against a gold answer
- An LLM-as-a-judge approach, scoring responses against defined criteria, integrated with MLflow evaluation
- Measuring CPU utilization only
- Counting the number of tokens generated
Show answer & explanation
Correct: B. For open-ended output with no single correct answer, LLM-as-a-judge scoring against defined criteria — run and tracked through MLflow evaluation — is the scalable, low-cost method the exam expects.
Why not the others: exact string match (A) fails on valid paraphrases; CPU utilization (C) is an infrastructure metric, not a quality measure; token count (D) measures length, not correctness or relevance. Evaluation and Monitoring centers on metrics like faithfulness, relevancy, and judge-based scoring.
Source: Databricks — Agent Evaluation (LLM judges) →Keep going: Learning & Career resources
This certification pays off fastest when it sits on top of real platform skills and a clear sense of where the role leads. Two PowerKram hubs back this exam up.
Deep dive: exam structure, scoring, study path & recertification
Exam structure and how it’s scored
The exam delivers 45 scored multiple-choice questions in 90 minutes; additional unscored items may appear for statistical calibration, with extra time factored in. Databricks does not publish a fixed numeric passing score — your result is reported as pass or fail. Most questions are scenario-based, asking you to choose the best architectural or tooling decision rather than recall a definition, so pacing matters even with a comparatively generous time budget. Read the exam-format deep dive →
What the six sections actually test
Application Development (30%) and Assembling and Deploying Applications (22%) together make up over half the exam, so the bulk of your preparation belongs in building LLM chains and RAG pipelines and in the MLflow → Unity Catalog → Model Serving deployment path. Design Applications and Data Preparation (14% each) cover problem decomposition and the retrieval data layer; Governance (8%) and Evaluation and Monitoring (12%) are lighter but still tested through guardrails, LLM-as-a-judge evaluation, and inference-table monitoring. Read the Databricks GenAI toolchain guide →
Realistic study path
Candidates commonly report roughly 20–30 hours of focused study, scaled by how much hands-on Databricks experience they already have. A workable plan: start with the free Generative AI Fundamentals course for background, work through the Generative AI Engineering with Databricks training, then build at least one end-to-end RAG application yourself — ingest and chunk documents, index them in Vector Search, compose a chain, deploy via Model Serving, and evaluate with MLflow. PowerKram’s section-level scoring surfaces which of the six areas is weakest so you can re-weight the final weeks. Read the study plan →
Cost, scheduling, and delivery
The registration fee is $200 USD plus applicable local taxes. The exam is proctored and can be taken online or at a test center, and is offered in English, Japanese, Portuguese (Brazil), and Korean. Online delivery requires a quiet private space and a system check through the proctoring provider. Register through the Databricks exam delivery platform. Verify current fees and scheduling on Databricks’ official page before booking. Databricks’ official certification page →
Recertification
The certification is valid for two years. To stay certified you retake and pass the current version of the exam before it expires — there is no PDU-style continuing-education path. Because Databricks refreshes the exam to track platform changes (recent editions add coverage of newer agent and serving features), recertifying also keeps your validated skills current. Read the recertification guide →
Career outlook
Generative AI engineering is one of the fastest-growing areas in the data and AI job market, and a platform-specific credential signals that you can ship production GenAI rather than only prototype it. The credential is most valuable paired with demonstrable project work — a deployed RAG application, an evaluated agent, a governed pipeline. For salary ranges and role-specific paths, see the Career Hub. Career Hub — AI & ML Engineer →
Frequently asked questions
Is the Generative AI Engineer Associate exam hard?
It is moderately challenging, and the difficulty is mostly about applied judgment rather than memorization. Most of the 45 questions are scenario-based — you are asked to pick the best architectural or tooling decision for a situation, not to recall a definition. Candidates who have actually built and deployed something on Databricks tend to find it fair; candidates who have only read about RAG and LLM chains tend to find the Application Development and deployment questions (over half the exam) the hardest part.
Can I take the exam without hands-on Databricks experience?
Yes — there are no formal prerequisites, so anyone can register and sit the exam. In practice it is difficult to pass cold. Databricks recommends around six months of hands-on experience with generative AI tasks, working Python, familiarity with a framework like LangChain, and exposure to MLflow, Unity Catalog, Vector Search, and Model Serving. If you are newer to the platform, building one end-to-end RAG application yourself closes most of the gap.
How long should I study for it?
Most candidates report roughly 20–30 hours of focused study, scaled by how much Databricks experience they already have. A workable plan is the free Generative AI Fundamentals course for background, then the Generative AI Engineering with Databricks training, then a self-built RAG project covering ingestion, Vector Search indexing, a chain, deployment via Model Serving, and evaluation with MLflow. Use practice questions to find your weakest of the six exam sections and re-weight your final weeks toward it.
Which Databricks tools does the exam focus on most?
Four come up repeatedly across sections: Vector Search for retrieval, Model Serving for deployment (including Foundation Model APIs and the pay-per-token versus provisioned-throughput trade-off), MLflow for logging, registration, and evaluation, and Unity Catalog for governance and model registration. Because Application Development (30%) and Assembling and Deploying Applications (22%) are the two heaviest sections, the build-and-deploy toolchain deserves the most practice.
Should I take it online or at a test center?
Both are valid and the exam content is identical. Online proctored delivery is convenient but requires a quiet, private room, a webcam check, and a passing system check through the proctoring provider beforehand — interruptions or background people can pause or void a session. A test center removes those environmental risks. Choose online only if you can guarantee a controlled space for the full 90 minutes.
Is this certification worth it?
For engineers working in or moving into production GenAI, it is a credible signal that you can ship LLM applications on Databricks rather than only prototype them — generative AI engineering is one of the fastest-growing areas of the data and AI job market. Its value is highest when paired with demonstrable project work, such as a deployed RAG application or an evaluated agent. At a $200 fee, the main cost is study time, and it is valid for two years before you recertify.
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