Databricks Generative AI Engineer Associate Exam Practice Questions, practice question pack
DATABRICKS · Generative AI Engineer Associate

Databricks Generative AI Engineer Associate Exam Practice Questions

109 questionsPDF by emailUpdated September 2026

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Three of the 109 questions in this pack

Question 1

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

  1. Change embedding models and compare performance.
  2. Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.
  3. Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters. Choose the strategy that gives the best performance metric.
  4. Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.
  5. Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.
Show answer and explanation

Correct answer: C, E

C. Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters. Choose the strategy that gives the best performance metric. E. Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric. Option C is correct because systematic evaluation using appropriate metrics (recall, NDCG) with experimentation on different chunking strategies (by paragraphs, chapters, token counts) provides empirical evidence for the best configuration. Option E is correct because using an LLM-as-a-judge metric directly evaluates retrieval quality by assessing how well chunks answer known questions, allowing data-driven optimization of chunking parameters. Both approaches replace intuition with measurable evaluation frameworks.

Why the other options are wrong

  • A. Changing embedding models addresses a different component and doesn't optimize the chunking strategy itself.
  • B. Adding a query classifier filters which book to search but doesn't optimize the chunking parameters.
  • D. Having an LLM predict ideal token counts lacks empirical validation and doesn't account for actual retrieval performance with real queries.

Question 2

A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.

What are the steps needed to build this RAG application and deploy it?

  1. Ingest documents from a source –> Index the documents and saves to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> Evaluate model –> LLM generates a response –> Deploy it using Model Serving
  2. Ingest documents from a source –> Index the documents and save to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> LLM generates a response -> Evaluate model –> Deploy it using Model Serving
  3. Ingest documents from a source –> Index the documents and save to Vector Search –> Evaluate model –> Deploy it using Model Serving
  4. User submits queries against an LLM –> Ingest documents from a source –> Index the documents and save to Vector Search –> LLM retrieves relevant documents –> LLM generates a response –> Evaluate model –> Deploy it using Model Serving
Show answer and explanation

Correct answer: B. Ingest documents from a source –> Index the documents and save to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> LLM generates a response -> Evaluate model –> Deploy it using Model Serving

documents and save to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> LLM generates a response -> Evaluate model –> Deploy it using Model Serving The correct sequence for RAG deployment is: ingest documents → index and save to vector store → user submits query → LLM retrieves relevant documents → LLM generates response → evaluate model → deploy. Option B maintains this logical flow where document preparation precedes user interaction, retrieval occurs before generation, and evaluation happens before deployment. This ensures the system is fully built and tested before production use.

Why the other options are wrong

  • A. Evaluating the model after generation but before deployment is correct, but this option shows evaluation occurring between retrieval and generation, which is premature.
  • C. This omits the critical user query submission and LLM response generation steps entirely.
  • D. Beginning with user query submission before documents are ingested and indexed is impossible since there would be no knowledge base to retrieve from.

Question 3

A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.

Which metric should they monitor for their customer service LLM application in production?

  1. Number of customer inquiries processed per unit of time
  2. Energy usage per query
  3. Final perplexity scores for the training of the model
  4. HuggingFace Leaderboard values for the base LLM
Show answer and explanation

Correct answer: A. Number of customer inquiries processed per unit of time

time For a customer service LLM application, monitoring the number of customer inquiries processed per unit of time is essential because it directly reflects the system's utility and throughput in a production environment. This operational metric helps track whether the application is meeting business needs and handling customer load effectively.

Why the other options are wrong

  • B. Energy usage is an infrastructure concern but not a primary metric for evaluating the application's performance in serving customer inquiries.
  • C. Final perplexity scores from training are static historical metrics that don't reflect real- time production quality or performance.
  • D. HuggingFace Leaderboard values reflect the base model's general capabilities, not the specific application's performance in its deployed context.

See all 10 free questions Get the full pack, US$39

109 practice questions for Databricks Certified Generative AI Engineer Associate, with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 109 questions mapped to the Generative AI Engineer Associate exam guide
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A Generative AI Engineer Associate attempt costs US$200. This pack is US$39, paid once.

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Last updated September 2026 · 109 questions

What makes the Generative AI Engineer Associate exam hard

Generative AI Engineer Associate is the fastest growing Databricks certification and the most scenario driven of them: 45 scored questions in 90 minutes, each describing a use case with a constraint, a latency budget, a data sensitivity or a cost ceiling, and four options that would all more or less run.

It is a decision-making exam about building RAG and agent applications on Databricks, with all code in Python, and the March 2026 edition of the guide reflects Mosaic AI Agent Framework, Vector Search and Model Serving as they stand now.

Application Development is the largest section at roughly 30%: building RAG chains and agents with LangChain and the Mosaic AI Agent Framework, prompt engineering and guardrails, tool calling, retrievers and rerankers, chunking strategies and embedding model selection. Assembling and Deploying Applications at roughly 22% covers packaging chains with MLflow, Unity Catalog registration, Model Serving endpoints and Vector Search index creation. The remaining sections cover design, data preparation, evaluation and monitoring, and governance, and the skill being tested is spotting the option that satisfies the constraint, not just the one that is technically sound.

About the exam

The Databricks Certified Generative AI Engineer Associate validates the ability to design, build, deploy, govern and evaluate generative AI applications such as RAG pipelines and agents on the Databricks Data Intelligence Platform. There are no prerequisites; Databricks recommends related training and six months of hands-on experience.

Exam sections (approximate weights from the Databricks exam guide)

  • Design Applications: about 14%
  • Data Preparation: about 14%
  • Application Development: about 30%
  • Assembling and Deploying Applications: about 22%
  • Governance: about 8%
  • Evaluation and Monitoring: about 12%

45 scored multiple choice questions plus possible unscored items, 90 minutes, passing score not published by Databricks (commonly cited as 70%), US$200 per attempt, online proctored or test centre, certification valid for two years.

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Questions before you buy

What do I get when I buy the Databricks Generative AI Engineer Associate pack?

109 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

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Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

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Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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