200+ Free Databricks GenAI Engineer Practice Questions

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Question 1

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An internal HR assistant must answer questions from policy documents that change every week and cite the source material. Which approach is the best starting point?

Fine-tune a chat model on past HR tickets

Build a RAG application over the policy documents

Train a sentiment classifier for each policy area

Hardcode the answers into the system prompt

to track

2026 Statistics

Key Facts: Databricks GenAI Engineer Exam

Scored Questions

Official exam page

90 min

Time Limit

Official exam page

$200

Exam Fee

Official exam page

30%

Largest Domain

Application Development

70%

Passing Benchmark

Databricks Academy FAQ

Mar 18, 2026

Blueprint Change

Interim exam guide

As of March 11, 2026, Databricks lists 45 scored questions, a 90-minute time limit, a $200 fee, and six weighted domains led by Application Development at 30% and Assembling and Deploying Apps at 22%. Databricks' Academy FAQ still states a 70% passing benchmark, which equals 32 correct answers out of 45. Databricks also published an interim exam guide noting objective changes that take effect on March 18, 2026, adding deeper emphasis on Agent Bricks, MCP integration, AI Gateway usage tracking, and agent-focused evaluation and deployment workflows.

Sample Databricks GenAI Engineer Practice Questions

Try these sample questions to test your Databricks GenAI Engineer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 200+ question experience with AI tutoring.

1An internal HR assistant must answer questions from policy documents that change every week and cite the source material. Which approach is the best starting point?

A.Fine-tune a chat model on past HR tickets

B.Build a RAG application over the policy documents

C.Train a sentiment classifier for each policy area

D.Hardcode the answers into the system prompt

Explanation: RAG is a strong first choice when the knowledge is proprietary and changes frequently because retrieval can use the latest indexed documents. It also supports source-aware answers much more naturally than fine-tuning or hardcoded prompts.

2A claims operations app must answer policy questions, retrieve supporting documents, and create a follow-up task in another system only when needed. Which architecture best fits this use case?

A.A prompt-only chatbot with no retrieval

B.A basic RAG chain with no tools

C.A tool-calling agent with retrieval and workflow tools

D.A fine-tuned embeddings model

Explanation: This workflow requires both knowledge retrieval and selective action taking, which is a good fit for an agent with tools. A plain RAG chain can answer questions, but it is not designed to decide when to call external systems.

3You need to convert document chunks into vectors before loading them into Mosaic AI Vector Search. Which model capability matters most?

A.Text embeddings generation

B.Reranking responses after generation

C.Image generation

D.Chat completion

Explanation: Embeddings models are built to map text into numeric vector space for similarity search. Chat models and image generators solve different tasks and are not the primary component for indexing text in a vector store.

4A team decomposed its app into intent classification, retrieval, and final answer generation. Which model-selection strategy usually lowers cost without hurting quality?

A.Use the largest available chat model for every step

B.Use smaller or specialized models for simpler steps and a stronger model only where needed

C.Fine-tune a separate model for each step before building a baseline

D.Avoid deterministic preprocessing so the LLM handles everything

Explanation: Problem decomposition lets you reserve expensive models for the steps that truly need deeper reasoning or stronger generation quality. Simpler sub-tasks such as classification or extraction can often be handled with cheaper models or task-specific functions.

5You need an LLM to return a stable JSON object with fields `issue_type`, `priority`, and `needs_human_review`. Which prompt change is most effective?

A.Ask the model to be creative

B.Provide the exact JSON schema and instruct it to return JSON only

C.Ask for a markdown table instead

D.Raise the temperature to encourage variety

Explanation: Explicit structure usually improves output reliability because the model has a concrete target format to follow. Telling it to return only JSON also reduces the chance of extra prose breaking downstream parsing.

6In a RAG app, users complain that the model confidently answers questions not covered by the retrieved context. Which prompt update is best?

A.Ask for longer and more detailed responses

B.Remove citations to simplify the prompt

C.Tell the model to answer only from the provided context and say it does not know otherwise

D.Increase chunk size until every answer fits in one chunk

Explanation: A grounding instruction helps the model treat retrieved context as the authority for the answer. It also creates a defined fallback behavior when the evidence is missing instead of encouraging hallucinated completions.

7You need a chatbot over an employee handbook with citations, but it does not need to take actions, request approvals, or plan across multiple tools. What is the simplest fit?

A.A tool-calling agent with calendar and ticketing tools

B.A fine-tuned classifier

C.A batch AI Functions pipeline

D.A RAG chain over the handbook documents

Explanation: A standard RAG chain is appropriate when the problem is primarily retrieval plus answer generation over a document set. Adding agent behavior would increase complexity without solving an actual requirement in this case.

8Where should a Databricks team usually store PDFs and other unstructured source files when building a governed RAG pipeline?

A.DBFS root

B.A notebook output folder

C.Unity Catalog volumes

D.Cluster local disk

Explanation: Unity Catalog volumes are the governed storage option for arbitrary files such as PDFs and images. They are a better fit than DBFS root or ephemeral cluster storage when the application needs persistence and access control.

9You try to create a standard Delta Sync vector search index from a Delta table and creation fails immediately. Which missing prerequisite is most likely?

A.The table was not vacuumed recently

B.Change Data Feed is not enabled on the source table

C.The table is not Z-ordered

D.The SQL warehouse is not classic

Explanation: Standard Delta Sync indexes rely on the source Delta table's change stream to keep the index updated. Without Change Data Feed enabled, the index cannot track incremental updates in the supported way.

10Why use chunk overlap when splitting long documents for RAG?

A.To make embeddings smaller

B.To avoid storing metadata

C.To eliminate the need for retrieval

D.To preserve context that spans chunk boundaries

Explanation: Important information often crosses a chunk boundary, especially in long paragraphs or lists. Overlap reduces the chance that the retriever returns a chunk missing the nearby text needed for a grounded answer.

About the Databricks GenAI Engineer Exam

The Databricks Certified Generative AI Engineer Associate exam validates your ability to design, build, govern, evaluate, and deploy LLM-enabled applications on Databricks. The public exam page emphasizes practical judgment around prompt design, RAG, chains and agents, Vector Search, Model Serving, MLflow, Unity Catalog, and ongoing monitoring rather than isolated memorization.

Assessment

45 scored multiple-choice or multiple-selection questions; unscored items may appear

Time Limit

90 minutes

Passing Score

70% (32/45) per Databricks Academy FAQ

Exam Fee

$200 (Databricks / Kryterion Webassessor)

Databricks GenAI Engineer Exam Content Outline

14%

Design Applications

Prompt design, problem decomposition, model-task selection, chain-component selection, and translating business goals into AI pipeline inputs and outputs.

14%

Data Preparation

Document extraction, filtering noisy source content, chunking, retrieval quality, Delta Lake and Unity Catalog data preparation, and retrieval design choices.

30%

Application Development

RAG chains, agents, tool use, LangChain and LangGraph-style workflows, guardrails, context injection, model and embedding selection, and agent-framework development.

22%

Assembling and Deploying Apps

Pyfunc packaging, MLflow registration, Vector Search configuration, Foundation Model API serving, deployment sequencing, persistent state, CI/CD, and interface selection.

Governance

Unity Catalog governance, masking, guardrails, data-source risk reduction, licensing awareness, and protective controls against unsafe or malicious inputs.

12%

Evaluation and Monitoring

Offline and online evaluation, inference logging, inference tables, Agent Monitoring, cost control, scoring judges, feedback incorporation, and live application monitoring.

How to Pass the Databricks GenAI Engineer Exam

What You Need to Know

Passing score: 70% (32/45) per Databricks Academy FAQ
Assessment: 45 scored multiple-choice or multiple-selection questions; unscored items may appear
Time limit: 90 minutes
Exam fee: $200

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Databricks GenAI Engineer Study Tips from Top Performers

1Study in weight order and spend the most time on Application Development and Assembling and Deploying Apps, because together they account for 52% of the exam.

2Build at least one small RAG application end to end on Databricks so chunking, embeddings, Vector Search, prompting, and response evaluation become procedural rather than theoretical.

3Practice choosing between prompting, RAG, tool use, and agentic workflows based on the actual business requirement instead of assuming every problem needs the same architecture.

4Know how MLflow, Unity Catalog, Model Serving, and Vector Search fit together, because Databricks expects platform-aware decisions rather than generic LLM knowledge.

5Treat retrieval quality as a first-class concern: chunk size, overlap, re-ranking, metadata filters, and embedding-model choice often determine whether a RAG answer succeeds.

6Learn the difference between offline evaluation and live monitoring, including when you need ground truth, when an LLM judge is appropriate, and what production metrics signal drift or failure.

7Review the March 18, 2026 objective changes before scheduling your exam if you are testing on or after that date, especially Agent Bricks, MCP integration, AI Gateway, and agent-monitoring workflows.

Frequently Asked Questions

How many questions are on the Databricks Generative AI Engineer Associate exam?

Databricks' official exam page lists 45 scored questions with a 90-minute time limit. The interim March 2026 exam guide says the scored questions can be multiple-choice or multiple-selection and that unscored items may also appear.

What is the current passing score?

Databricks' public exam page does not publish an exam-specific passing threshold, but the Databricks Academy FAQ PDF says certification exams require an unrounded score of 70.00% or better. For a 45-question exam, that equals 32 correct answers.

Which domains matter most?

Application Development is the biggest block at 30%, followed by Assembling and Deploying Apps at 22%. Design Applications and Data Preparation are 14% each, Evaluation and Monitoring is 12%, and Governance is 8%, so RAG and agent implementation depth should drive the majority of your study time.

What changed on March 18, 2026?

Databricks published an interim exam guide stating that the blueprint changes on March 18, 2026. The updated objectives add deeper coverage of Agent Bricks, managed or external MCP server integration, AI Gateway usage tracking, custom scorers, interactive agent interfaces, CI/CD for prompts and vector indexes, and broader agent-evaluation workflows.

Do I need hands-on Databricks experience?

Yes. Databricks recommends related training plus roughly six months of hands-on experience implementing generative AI functionality on the platform. The exam is strongly scenario-based, so practical familiarity with Vector Search, Model Serving, MLflow, Unity Catalog, and agent or RAG workflows matters a lot.

Which Databricks tools should I know best?

The most important tools are Vector Search, Model Serving, MLflow, Unity Catalog, inference tables, and Databricks' agent and evaluation tooling. You should also be comfortable reasoning about LangChain or LangGraph-style orchestration, retrieval quality, prompt engineering, guardrails, and cost or latency tradeoffs.