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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.

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Questions updated at Aug 23, 2026, 1:05 AM 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 chain components for a desired model input and output Design Applications

A chat application must preserve the user's question, rewrite it for retrieval, keep the original wording for the final answer, and expose retrieval latency in traces. Which components are required?

Concept tested:
Question 2 of 10
Objective Apply prompt version control and manage prompt lifecycle Assembling and Deploying Applications

Production code currently hard-codes prompt version 17. The team wants staging to test a new prompt and production to roll back without rebuilding application code. Which pattern is best?

Concept tested:
Question 3 of 10
Objective Define and order tools that gather knowledge or take actions for multi-stage reasoning Design Applications

A troubleshooting agent can read telemetry, query a runbook index, and restart a service. Restarts require both evidence of the known failure signature and a matching approved runbook step. What tool flow should be designed?

Concept tested:
Question 4 of 10
Objective Develop an interactive user-facing interface for an agent scenario Assembling and Deploying Applications

A background process in a Databricks App writes application telemetry to a shared governed table, while interactive user queries must respect user-specific UC permissions. Which identity design is best?

Concept tested:
Question 5 of 10
Objective Select a model from a model hub or marketplace based on model metadata or model cards Application Development

A security review requires knowing whether a candidate model's terms permit processing the planned data and whether the deployment option keeps data within approved controls. Which model-card details are most important before benchmarking?

Concept tested:
Question 6 of 10
Objective Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application Governance

A RAG source contains repeated discriminatory language because it archives historical complaints. The application only needs factual incident categories, not the offensive quotations. Which mitigation is strongest?

Concept tested:
Question 7 of 10
Objective Use tools and metrics to evaluate retrieval performance Data Preparation

A RAG system returns highly relevant chunks when it finds them, but in 18% of labeled questions none of the required evidence appears in the top 10. Which metric/problem should the team focus on first?

Concept tested:
Question 8 of 10
Objective Use Databricks custom Scorers for evaluating agents and LLMs Evaluation and Monitoring

A legal drafting assistant must include a jurisdiction-specific disclaimer whose exact wording is deterministically defined. Which scorer design is best?

Concept tested:
Question 9 of 10
Objective Augment a prompt with context from user input based on key fields, terms, and intents Application Development

A banking assistant detects that a user intent is `lost_card`. Policy requires a specific warning and prohibits troubleshooting steps from the generic FAQ corpus. Which prompt/retrieval augmentation is best?

Concept tested:
Question 10 of 10
Objective Select guardrail techniques to protect against malicious user inputs Governance

An agent has a file-delete tool. Security testing shows the model can be induced to delete more files than requested by broad patterns. Which guardrail is most important at the tool boundary?

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 chat application must preserve the user's question, rewrite it for retrieval, keep the original wording for the final answer, and expose retrieval latency in traces. Which components are required?

Answer choices

  1. A. A query-rewrite step, retriever, prompt composer that retains the original question, generator, and MLflow tracing around the chain steps.
  2. B. A generator plus a final regex parser; tracing is unnecessary when latency is the only metric.
  3. C. A retriever only, because the embedding model can both rewrite and answer the question.
  4. D. A multi-agent supervisor with no explicit retriever because subagents can infer the knowledge.

Correct answer

A query-rewrite step, retriever, prompt composer that retains the original question, generator, and MLflow tracing around the chain steps.

The chain needs distinct query-understanding, retrieval, augmentation, generation, and tracing responsibilities to satisfy all stated requirements.

Wrong-answer review

  • B. A generator plus a final regex parser; tracing is unnecessary when latency is the only metric.: A generator and parser omit evidence retrieval and the requested step-level latency visibility.
  • C. A retriever only, because the embedding model can both rewrite and answer the question.: A retriever does not perform all of rewriting, response generation, and trace instrumentation.
  • D. A multi-agent supervisor with no explicit retriever because subagents can infer the knowledge.: Orchestration does not replace a defined retrieval component or guarantee traceability.

Extra learning features

Why candidates miss this

The distractor is plausible because it sounds like a quick fix: A generator plus a final regex parser; tracing is unnecessary when latency is the only metric. The decisive clue is the requirement in the scenario, not the convenience of that shortcut. Map each input/output/observability requirement to a specific chain component; do not remove a required stage merely to simplify the architecture. Likely wrong answer: A generator plus a final regex parser; tracing is unnecessary when latency is the only metric. Review focus: Build a high-quality RAG chain

Objective/domain: Design Applications

Source: Build a high-quality RAG chain

Question 2 Production code currently hard-codes prompt version 17. The team wants staging to test a new prompt and production to roll back without rebuilding application code. Which pattern is best?

Answer choices

  1. A. Hard-code the latest numeric version in every deployment and rebuild the service for each promotion.
  2. B. Edit version 17 in place for staging, then copy the text back if production needs rollback.
  3. C. Load prompts by environment-specific aliases (for example staging/prod) that point to immutable prompt versions and move aliases only after evaluation gates pass.
  4. D. Store the prompt only in the model source code because external prompt versioning prevents reproducibility.

Correct answer

Load prompts by environment-specific aliases (for example staging/prod) that point to immutable prompt versions and move aliases only after evaluation gates pass.

Objective/domain: Assembling and Deploying Applications

Source: Use prompts in deployed applications

Question 3 A troubleshooting agent can read telemetry, query a runbook index, and restart a service. Restarts require both evidence of the known failure signature and a matching approved runbook step. What tool flow should be designed?

Answer choices

  1. A. Retrieve a runbook first, restart if any restart instruction is present, and inspect telemetry afterward.
  2. B. Run telemetry and runbook retrieval in any order, but give the restart tool permission to self-authorize.
  3. C. Read telemetry, retrieve the relevant runbook, verify the signature and authorized action, then invoke restart; otherwise escalate.
  4. D. Restart whenever telemetry shows high latency because remediation should precede diagnosis.

Correct answer

Read telemetry, retrieve the relevant runbook, verify the signature and authorized action, then invoke restart; otherwise escalate.

Objective/domain: Design Applications

Source: Agent system design patterns

Question 4 A background process in a Databricks App writes application telemetry to a shared governed table, while interactive user queries must respect user-specific UC permissions. Which identity design is best?

Answer choices

  1. A. Use user authorization for the background job even when no user is present and store the last user's token.
  2. B. Use app authorization/service principal for the shared background telemetry and user authorization for interactive data operations that require the caller's permissions.
  3. C. Use only the app service principal for all operations and reimplement row-level permissions in application code.
  4. D. Use no OAuth and grant CAN MANAGE to all app users.

Correct answer

Use app authorization/service principal for the shared background telemetry and user authorization for interactive data operations that require the caller's permissions.

Objective/domain: Assembling and Deploying Applications

Source: Configure authorization in a Databricks app

Question 5 A security review requires knowing whether a candidate model's terms permit processing the planned data and whether the deployment option keeps data within approved controls. Which model-card details are most important before benchmarking?

Answer choices

  1. A. Training compute estimate only, because larger training runs imply stronger governance.
  2. B. License/usage terms, supported deployment/data-handling characteristics, and relevant capability constraints.
  3. C. Parameter count, marketing popularity, and benchmark leaderboard rank only.
  4. D. Tokenizer vocabulary size only, because it determines legal usage.

Correct answer

License/usage terms, supported deployment/data-handling characteristics, and relevant capability constraints.

Objective/domain: Application Development

Source: Foundation Model APIs

Question 6 A RAG source contains repeated discriminatory language because it archives historical complaints. The application only needs factual incident categories, not the offensive quotations. Which mitigation is strongest?

Answer choices

  1. A. Ask the generator to ignore offensive text but retrieve it normally.
  2. B. Index the raw text unchanged because historical accuracy always requires exposing every word to the model.
  3. C. Delete the entire source dataset even though the required factual fields are useful and can be separated.
  4. D. Transform/extract the required structured facts while excluding or masking unnecessary harmful text from the retrieval corpus, preserving governed provenance to the source.

Correct answer

Transform/extract the required structured facts while excluding or masking unnecessary harmful text from the retrieval corpus, preserving governed provenance to the source.

Objective/domain: Governance

Source: Build a high-quality RAG data pipeline

Question 7 A RAG system returns highly relevant chunks when it finds them, but in 18% of labeled questions none of the required evidence appears in the top 10. Which metric/problem should the team focus on first?

Answer choices

  1. A. Generation fluency, because well-written answers can compensate for missing retrieval evidence.
  2. B. Recall/coverage of required evidence in the retrieved candidate set, using labeled queries that identify the relevant source chunks.
  3. C. Prompt length, because shorter prompts always improve retrieval recall.
  4. D. Model-serving throughput, because more QPS increases the probability of finding relevant chunks.

Correct answer

Recall/coverage of required evidence in the retrieved candidate set, using labeled queries that identify the relevant source chunks.

Objective/domain: Data Preparation

Source: Evaluate and monitor GenAI applications

Question 8 A legal drafting assistant must include a jurisdiction-specific disclaimer whose exact wording is deterministically defined. Which scorer design is best?

Answer choices

  1. A. Use model latency as the disclaimer compliance metric, assuming a response that meets the serving SLO also contains the required jurisdiction-specific wording.
  2. B. Use a code-based custom scorer that deterministically checks the required disclaimer, alongside other semantic judges as needed.
  3. C. Use RetrievalRelevance because a relevant document guarantees the output contains the required text.
  4. D. Use a general safety judge only, because deterministic text requirements should never be coded.

Correct answer

Use a code-based custom scorer that deterministically checks the required disclaimer, alongside other semantic judges as needed.

Objective/domain: Evaluation and Monitoring

Source: Custom scorers for GenAI evaluation

Question 9 A banking assistant detects that a user intent is `lost_card`. Policy requires a specific warning and prohibits troubleshooting steps from the generic FAQ corpus. Which prompt/retrieval augmentation is best?

Answer choices

  1. A. Add every policy to the prompt so the model can choose which one applies.
  2. B. Retrieve the generic FAQ corpus normally and add 'lost_card' as a weak keyword.
  3. C. Route the lost-card intent to the approved lost-card context/policy and inject the required warning as trusted instruction, excluding incompatible generic troubleshooting content.
  4. D. Let the model infer the safety warning from pretrained banking knowledge.

Correct answer

Route the lost-card intent to the approved lost-card context/policy and inject the required warning as trusted instruction, excluding incompatible generic troubleshooting content.

Objective/domain: Application Development

Source: Build a high-quality RAG chain

Question 10 An agent has a file-delete tool. Security testing shows the model can be induced to delete more files than requested by broad patterns. Which guardrail is most important at the tool boundary?

Answer choices

  1. A. Increase the LLM context window so it can remember not to delete extra files.
  2. B. Validate and constrain tool arguments against the user's authorized scope, use narrow schemas, and require confirmation for destructive/bulk operations.
  3. C. Make the tool description longer so the model feels more certain.
  4. D. Log deleted file names after execution and use the logs as the primary preventive control.

Correct answer

Validate and constrain tool arguments against the user's authorized scope, use narrow schemas, and require confirmation for destructive/bulk operations.

Objective/domain: Governance

Source: MCP Services on Databricks

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