Databricks GENAI-ASSOC Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Databricks Generative AI Engineer Associate (GENAI-ASSOC) topics such as RAG design, chunking, Vector Search, MLflow, Agent Framework, governance, monitoring, and evaluation. The prompts emphasize system trade-offs rather than prompt wording tricks.

Where these questions fit in the GENAI-ASSOC guide

The sample set below is part of the Databricks GENAI-ASSOC guide path:

• Databricks certification hub
• GENAI-ASSOC exam guide
• GENAI-ASSOC cheat sheet
• GENAI-ASSOC study plan
• GENAI-ASSOC FAQ
• GENAI-ASSOC resources
• GENAI-ASSOC glossary

GENAI-ASSOC generative AI sample questions

Work through each prompt before opening the explanation. GENAI-ASSOC questions usually reward answers that improve retrieval quality, evaluation evidence, governance, and production observability before escalating model size or prompt complexity.

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Question 1

Topic: Improving weak RAG answers

A support assistant built on Databricks gives fluent but incomplete answers for policy questions. Review shows the model is capable, but the retrieved chunks often miss the relevant paragraph or include too much unrelated text. What should the team improve first?

• A. Increase the generation temperature so the model explores more possible answers.
• B. Disable retrieval and rely on the foundation model’s general knowledge.
• C. Review document parsing, chunk size, overlap, metadata filters, and retrieval evaluation before changing the model.
• D. Move every full source document into the prompt context for each request.

Best answer: C

Explanation: The failure is retrieval quality, not generation creativity. Better parsing, chunking, metadata, filtering, and retrieval evaluation directly target missing or noisy context.

Why the other choices are weaker:

• A can make answers less stable and does not repair the evidence supplied to the model.
• B removes grounding and increases hallucination risk.
• D is expensive, noisy, and often hurts relevance rather than improving it.

What this tests: RAG diagnosis, chunking, retrieval evaluation, metadata filters, and grounding discipline.

Related topics: RAG; Chunking; Vector Search; Evaluation

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Question 2

Topic: Choosing an evaluation signal

A team is preparing a RAG assistant for internal release. Stakeholders want evidence that answers are grounded in approved sources and that failures can be debugged. Which evaluation and monitoring approach is strongest?

• A. Track only average response latency because fast responses indicate a healthy application.
• B. Ask developers to manually read a few successful conversations after deployment.
• C. Use labeled examples, retrieval-quality checks, answer-quality judges or rubrics, traces, and inference logs tied to prompts, context, and outputs.
• D. Measure only token volume because low token usage proves the answers are correct.

Best answer: C

Explanation: GENAI-ASSOC expects quality to be measured across retrieval, answer behavior, traceability, and production signals. Logs and traces make failures diagnosable instead of anecdotal.

Why the other choices are weaker:

• A measures performance, not factuality or grounding.
• B is not systematic enough for release confidence.
• D confuses cost control with correctness.

What this tests: evaluation design, tracing, inference logging, quality rubrics, and grounded-answer monitoring.

Related topics: Monitoring; Tracing; Inference logs; Quality metrics

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Question 3

Topic: Governed agent tool access

An agent workflow needs to call a ticketing tool and query governed enterprise data. Security reviewers require least privilege, auditability, and prevention of unrestricted tool calls. Which design best fits the requirement?

• A. Give the agent a broad service credential and rely on prompt instructions to prevent misuse.
• B. Let the model construct arbitrary API calls because that gives the agent maximum flexibility.
• C. Copy all ticket and enterprise data into the prompt so the agent does not need tools.
• D. Use governed identities, scoped tool permissions, Unity Catalog controls where data is involved, logging, and explicit tool schemas or guardrails.

Best answer: D

Explanation: Tool use must be governed like any other production integration. Scoped permissions, governed data access, explicit tool contracts, and logs reduce risk while preserving useful agent behavior.

Why the other choices are weaker:

• A over-grants access and treats prompting as a security boundary.
• B creates uncontrolled action risk and weak auditability.
• C increases data exposure and does not solve workflow action control.

What this tests: agent tool use, governance, Unity Catalog, least privilege, logging, and guardrails.

Related topics: Agents; Tool use; Unity Catalog; Governance

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Question 4

Topic: Deploying a reviewable GenAI app

A prototype chain works in a notebook. The team now needs repeatable deployment, versioned artifacts, reviewable changes, and rollback capability. Which next step is strongest?

• A. Package the chain or agent with an appropriate deployment workflow, track versions with MLflow or Databricks lifecycle tooling, and use controlled serving endpoints and CI/CD review.
• B. Keep editing the notebook directly in production because the chain already works.
• C. Paste the current prompt into a shared document and ask users to copy it manually.
• D. Only increase endpoint concurrency because deployment is mainly a scaling problem.

Best answer: A

Explanation: Moving from prototype to production requires versioning, packaging, review, serving discipline, and rollback paths. GENAI-ASSOC questions often test that deployment is an operating model, not a notebook handoff.

Why the other choices are weaker:

• B is not reviewable or repeatable enough for production.
• C abandons the application lifecycle and creates inconsistent behavior.
• D handles capacity but not versioning, governance, or rollout safety.

What this tests: MLflow, deployment lifecycle, serving endpoints, CI/CD, versioning, and rollback thinking.

Related topics: MLflow; Deployment; Serving; CI/CD

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