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Databricks Certified Generative AI Engineer Associate Practice Test

Databricks Generative AI Engineer Associate Practice Test

Start today’s free 10-question Databricks Generative AI Engineer Associate set with source-backed explanations, local progress, and a fresh rotation every morning.

10 Free Daily Questions Source-backed Explanations 200 Verified Questions

Questions updated at Aug 23, 2026, 8:12 PM CDT

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Today's 10 Databricks Generative AI Engineer Associate questions

Use this Databricks Generative AI Engineer Associate practice test to review Databricks Certified Generative AI Engineer Associate. Questions rotate daily and each answer links back to the source used to write it.

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Question 1 of 10
Objective Select an embedding model context length based on source documents, queries, and optimization strategy Application Development

A corpus uses 700-token coherent chunks. Candidate embedding model A accepts 512 tokens; model B accepts 1,024. Truncation tests show relevant facts often occur after token 512. Which choice is most defensible if chunking must remain unchanged?

Concept tested:
Question 2 of 10
Objective Apply CI/CD practices for Vector Search updates, prompt promotion, and agent component testing Assembling and Deploying Applications

A Vector Search source schema changes. The team wants CI/CD to catch failures before production. Which test is most targeted?

Concept tested:
Question 3 of 10
Objective Select model tasks to accomplish a given business requirement Design Applications

A product team wants to triage 50,000 feedback comments into a fixed taxonomy, then draft a short response only for comments in the 'urgent defect' class. Which task sequence is most appropriate?

Concept tested:
Question 4 of 10
Objective Select an LLM choice based on quantitative evaluation metrics Evaluation and Monitoring

Two models have equivalent correctness and groundedness. Model A costs 45% less but uses 30% more tokens because its per-token price is lower; both meet the latency SLO. Which metric should drive the choice?

Concept tested:
Question 5 of 10
Objective Use legal and licensing requirements for data sources to avoid legal risk Governance

A team wants to ingest code from multiple open-source repositories into a coding assistant. Licenses differ, and generated output may reproduce snippets. Which governance practice is strongest?

Concept tested:
Question 6 of 10
Objective Identify source documents that provide necessary knowledge and quality for a RAG application Data Preparation

A troubleshooting assistant performs well on installation questions but fails on rare error codes. Corpus review shows user manuals but no service bulletins. What is the best next step?

Concept tested:
Question 7 of 10
Objective Create a prompt that adjusts an LLM response from a baseline to a desired output Application Development

A marketing generator is on-brand but too verbose. Reviewers want 60-90 words, one CTA, and no superlatives. Which prompt modification is most direct?

Concept tested:
Question 8 of 10
Objective Control access to resources from model serving endpoints Assembling and Deploying Applications

A team wants analysts to call an endpoint but not change its configuration. Which permission model is appropriate?

Concept tested:
Question 9 of 10
Objective Select chain components for a desired model input and output Design Applications

A chain takes a user query, retrieves 30 candidate chunks, must improve the ordering for the top five context passages, and then generate an answer. Which component belongs between retrieval and prompt augmentation?

Concept tested:
Question 10 of 10
Objective Select key metrics to monitor for a specific LLM deployment scenario Evaluation and Monitoring

A RAG application remains fast and cheap, but users report increasingly unsupported answers after source documents change. Which monitoring metric should be added or emphasized?

Concept tested:
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The free daily Databricks Generative AI Engineer Associate set includes crawlable question text, answer choices, correct answer labels, objective mapping, and source links. Only the first SEO card includes answer explanations and any extra learning features. Pro-only bank questions stay locked; this section mirrors only the 10 free daily questions already shown on this page.

Question 1 A corpus uses 700-token coherent chunks. Candidate embedding model A accepts 512 tokens; model B accepts 1,024. Truncation tests show relevant facts often occur after token 512. Which choice is most defensible if chunking must remain unchanged?

Answer choices

  1. A. Use model B because its input limit can represent the full chunk, then validate retrieval quality and cost on the target query set.
  2. B. Use model A because shorter embedding context always improves semantic precision, for the described technical objective and its associated operational control requirements, within the proposed design.
  3. C. Choose either model solely by vector dimensionality because input length does not affect embeddings, for the described technical objective and its associated operational control requirements.
  4. D. Use model A and assume the generator's larger context window recovers omitted text, for the described technical objective and its associated operational control requirements.

Correct answer

Use model B because its input limit can represent the full chunk, then validate retrieval quality and cost on the target query set.

An embedding model must be able to consume the content the indexing strategy expects it to represent; otherwise systematic truncation can hide relevant evidence.

Wrong-answer review

  • B. Use model A because shorter embedding context always improves semantic precision, for the described technical objective and its associated operational control requirements, within the proposed design.: Shorter supported context is not inherently more precise when required content is truncated.
  • C. Choose either model solely by vector dimensionality because input length does not affect embeddings, for the described technical objective and its associated operational control requirements.: Embedding dimensionality and maximum input context are separate constraints.
  • D. Use model A and assume the generator's larger context window recovers omitted text, for the described technical objective and its associated operational control requirements.: The generator cannot retrieve text that the index vector failed to represent.

Extra learning features

Why candidates miss this

The distractor is plausible because it sounds like a quick fix: Use model A because shorter embedding context always improves semantic precision. The decisive clue is the requirement in the scenario, not the convenience of that shortcut. Avoid systematic embedding truncation, but choose context length jointly with chunking and measured retrieval quality—not by assuming longer or shorter is always superior. Likely wrong answer: Use model A because shorter embedding context always improves semantic precision. Review focus: Build a high-quality RAG data pipeline

Interview question

Q: If your chunks are 700 tokens and relevant facts often occur after token 512, how would you choose between 512- and 1,024-token embedding models when chunking cannot change? Strong answer: Choose the model that can represent the content the indexing strategy expects—in this case the 1,024-token candidate—assuming it passes the rest of the evaluation. Otherwise systematic truncation can hide relevant evidence before retrieval ever runs.

  • embedding truncation
  • chunk/model alignment
  • relevant evidence after limit
  • evaluation of eligible model

Caution: Look for reasoning tied to the stated Databricks component and trade-off; a product name without the decision logic is incomplete.

Objective/domain: Application Development

Source: Build a high-quality RAG data pipeline

Question 2 A Vector Search source schema changes. The team wants CI/CD to catch failures before production. Which test is most targeted?

Answer choices

  1. A. Disable metadata filters so schema changes cannot break them, within the documented operational, security, ownership, and validation requirements.
  2. B. Run component tests that create/synchronize the index against the new schema and execute representative filtered queries before promotion, for the required outcome.
  3. C. Increase endpoint capacity in production as a preventive measure, for the described technical objective and its associated operational control requirements.
  4. D. Run only a spelling check on the prompt because index schemas do not affect retrieval, under organization-wide implementation-governance requirements.

Correct answer

Run component tests that create/synchronize the index against the new schema and execute representative filtered queries before promotion, for the required outcome.

Objective/domain: Assembling and Deploying Applications

Source: Create a Vector Search index

Question 3 A product team wants to triage 50,000 feedback comments into a fixed taxonomy, then draft a short response only for comments in the 'urgent defect' class. Which task sequence is most appropriate?

Answer choices

  1. A. Embed all comments and choose the nearest response template without classifying intent, as described.
  2. B. Use a multi-agent conversation for each comment so agents can debate the category, for the affected environment.
  3. C. Classify each comment first; invoke generation only for comments classified as urgent defects, when applied.
  4. D. Generate a response for every comment, then classify the generated responses, under the documented operational and governance requirements.

Correct answer

Classify each comment first; invoke generation only for comments classified as urgent defects, when applied.

Objective/domain: Design Applications

Source: Agent system design patterns

Question 4 Two models have equivalent correctness and groundedness. Model A costs 45% less but uses 30% more tokens because its per-token price is lower; both meet the latency SLO. Which metric should drive the choice?

Answer choices

  1. A. Total measured cost per successful request/workload, not token count alone, because both quality and latency constraints are already met, for consideration.
  2. B. Average answer length only, because concise responses are always cheaper and better, for the described technical objective and its associated operational control requirements.
  3. C. Parameter count, because larger models are safer tie-breakers, for the described technical objective and its associated operational control requirements, for this decision.
  4. D. Raw token count only, because fewer tokens always means lower cost, under the organization’s defined implementation and exception-management process.

Correct answer

Total measured cost per successful request/workload, not token count alone, because both quality and latency constraints are already met, for consideration.

Objective/domain: Evaluation and Monitoring

Source: Evaluate and monitor GenAI applications

Question 5 A team wants to ingest code from multiple open-source repositories into a coding assistant. Licenses differ, and generated output may reproduce snippets. Which governance practice is strongest?

Answer choices

  1. A. Record source provenance and license metadata, define allowed uses/attribution obligations by license, and filter ineligible repositories before indexing, for the required outcome.
  2. B. Index everything and review licenses only after a customer reports a problem, within the documented operational, security, ownership, and validation requirements.
  3. C. Assume all code on a public repository is public domain, for the described technical objective and its associated operational control requirements, for the specified implementation requirement.
  4. D. Strip repository metadata so the model cannot reveal where code originated, for the described technical objective and its associated operational control requirements, within this design.

Correct answer

Record source provenance and license metadata, define allowed uses/attribution obligations by license, and filter ineligible repositories before indexing, for the required outcome.

Objective/domain: Governance

Source: AI governance with Unity Catalog and Unity AI Gateway

Question 6 A troubleshooting assistant performs well on installation questions but fails on rare error codes. Corpus review shows user manuals but no service bulletins. What is the best next step?

Answer choices

  1. A. Add the authoritative service bulletins that contain the missing error-code knowledge, then re-run the failing evaluation set, for the required business outcome.
  2. B. Replace the embedding model before changing the source corpus, for the described technical objective and its associated operational control requirements, for this task.
  3. C. Increase top-k on the existing manual corpus even though the needed facts are absent, under organization-wide implementation-governance requirements.
  4. D. Increase the generator temperature so it can infer likely error meanings, within the documented operational, security, ownership, and validation requirements.

Correct answer

Add the authoritative service bulletins that contain the missing error-code knowledge, then re-run the failing evaluation set, for the required business outcome.

Objective/domain: Data Preparation

Source: Build a high-quality RAG data pipeline

Question 7 A marketing generator is on-brand but too verbose. Reviewers want 60-90 words, one CTA, and no superlatives. Which prompt modification is most direct?

Answer choices

  1. A. Add explicit length, CTA, and prohibited-language constraints and include an example that satisfies all three, for the stated application development requirement.
  2. B. Increase max_tokens and rely on users to stop reading at 90 words, within the documented scope, ownership, and validation boundaries.
  3. C. Lower top-k in Vector Search so the model has less information to discuss, within the stated policy framework.
  4. D. Use a larger context window so the model can understand brevity better, as the recommended response to this scenario.

Correct answer

Add explicit length, CTA, and prohibited-language constraints and include an example that satisfies all three, for the stated application development requirement.

Objective/domain: Application Development

Source: Prompt engineering best practices

Question 8 A team wants analysts to call an endpoint but not change its configuration. Which permission model is appropriate?

Answer choices

  1. A. Grant management permission to every caller because query access does not support authentication, under the organization’s defined implementation and exception-management process.
  2. B. Make the endpoint public and enforce authorization only in the prompt, within the documented operational, security, ownership, and validation requirements.
  3. C. Use a single shared administrator token in the client application, for the described technical objective and its associated operational control requirements, within the described context.
  4. D. Grant the minimum query/use permission needed to invoke the endpoint and reserve management permissions for the operators who administer it, under the documented operational and governance requirements.

Correct answer

Grant the minimum query/use permission needed to invoke the endpoint and reserve management permissions for the operators who administer it, under the documented operational and governance requirements.

Objective/domain: Assembling and Deploying Applications

Source: Databricks Model Serving

Question 9 A chain takes a user query, retrieves 30 candidate chunks, must improve the ordering for the top five context passages, and then generate an answer. Which component belongs between retrieval and prompt augmentation?

Answer choices

  1. A. An additional embedding job that rewrites every source document at request time, within the defined security and accountability boundaries.
  2. B. A second vector index containing the same vectors but no metadata, for the described technical objective and its associated operational control requirements.
  3. C. A post-generation judge that changes the order of citations after the answer is written, for the affected environment.
  4. D. A reranker that scores the retrieved candidates and selects the strongest passages before prompt construction, within the stated policy framework.

Correct answer

A reranker that scores the retrieved candidates and selects the strongest passages before prompt construction, within the stated policy framework.

Objective/domain: Design Applications

Source: Build a high-quality RAG chain

Question 10 A RAG application remains fast and cheap, but users report increasingly unsupported answers after source documents change. Which monitoring metric should be added or emphasized?

Answer choices

  1. A. Average response length, because unsupported answers are always longer, as the primary implementation for the described business requirement.
  2. B. CPU utilization only, because quality drift originates from resource pressure, for the stated evaluation and monitoring requirement.
  3. C. A groundedness/correctness quality scorer on sampled production traces, alongside retrieval-quality signals for the changed corpus, within the proposed design.
  4. D. Number of prompt versions, because more versions guarantees higher hallucination risk, as the recommended implementation across the complete governed service lifecycle.

Correct answer

A groundedness/correctness quality scorer on sampled production traces, alongside retrieval-quality signals for the changed corpus, within the proposed design.

Objective/domain: Evaluation and Monitoring

Source: Evaluate and monitor GenAI applications

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