PracticeStudy GuidesBlogFlashcardsEspañol
All Practice Exams

200+ Free Databricks GenAI Engineer Practice Questions

Pass your Databricks Certified Generative AI Engineer Associate exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
Not publicly published Pass Rate
200+ Questions
100% Free

Loading practice questions...

2026 Statistics

Key Facts: Databricks GenAI Engineer Exam

45

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.

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.

8%

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.