Databricks Certified Generative AI Engineer Associate (Generative AI Engineer Associate) practice exam and study path
A free, timed 60-question Generative AI Engineer Associate practice exam in 90 minutes, scored by domain, with every option explained after you submit. Also 148 practice questions you can check one at a time, a page for every task in the exam guide, and a study path. No sign-up.
$6.99for 12 months
The Databricks generative AI engineering exam: designing prompts, chains and agents, preparing and chunking documents for retrieval, building and evaluating RAG applications and agents with MLflow, serving them on Model Serving and AI Search, governing them with Unity Catalog and guardrails, and monitoring quality and cost in production.
The exam
- Exam code
- Generative AI Engineer Associate
- Questions
- Not yet published
- Time
- 90 minutes
- Passing score
- Not published
- Exam fee
- $200 (USD)
- Question types
- multiple choice, multiple response
45 scored multiple-choice and multiple-selection questions in 90 minutes (up to 10 unscored items may be added and are not identified), USD 200 plus applicable taxes, online proctored (the certification page also lists test centers), no test aids, in English, Japanese, Portuguese (BR) and Korean. No prerequisite; related training and six months of hands-on Databricks experience are recommended. Valid for 2 years; to recertify you take the current exam. Passing score: Not published. This outline is the version published March 18, 2026.
From Databricks's certification page and exam guide, checked Nov 1, 2026.
Domains and weights
Domain 1: Design Applications
14%
Domain 2: Data Preparation
14%
Domain 3: Application Development
30%
Domain 4: Assembling and Deploying Applications
22%
Domain 5: Governance
8%
Domain 6: Evaluation and Monitoring
12%
On our roadmap this comes after Data Engineer Associate. See every certification.
Practice
148 original practice questions, every option explained with the Databricks documentation page that proves it. Free, no sign-up; progress stays on this device.
148 questions · practice mode
All domains
Every sample question for this exam, in domain order.
20 questions · practice mode
Topic quiz — Domain 1: Design Applications
A topic quiz on domain 1 (14% of the exam).
25 questions · practice mode
Topic quiz — Domain 2: Data Preparation
A topic quiz on domain 2 (14% of the exam).
38 questions · practice mode
Topic quiz — Domain 3: Application Development
A topic quiz on domain 3 (30% of the exam).
33 questions · practice mode
Topic quiz — Domain 4: Assembling and Deploying Applications
A topic quiz on domain 4 (22% of the exam).
13 questions · practice mode
Topic quiz — Domain 5: Governance
A topic quiz on domain 5 (8% of the exam).
19 questions · practice mode
Topic quiz — Domain 6: Evaluation and Monitoring
A topic quiz on domain 6 (12% of the exam).
60 questions · 90 min · timed
Full practice exam
The real exam's pace, no feedback until you submit.
60 questions · 90 min · timed · exam pack
Practice exam 1
Included in the Generative AI Engineer Associate exam pack.
60 questions · 90 min · timed · exam pack
Practice exam 2
Included in the Generative AI Engineer Associate exam pack.
60 questions · 90 min · timed · exam pack
Practice exam 3
Included in the Generative AI Engineer Associate exam pack.
More practice exams
The lessons, the sample questions on every task page and the first full practice exam are free and stay free. A pack adds the further timed Generative AI Engineer Associate practice exams.
Generative AI Engineer Associate exam pack
$6.99for 12 months
- Every paid Generative AI Engineer Associate practice exam, timed, with every option explained
- The next version of this exam included when it changes within your 12 months
- 12 months of access. One payment, nothing renews by itself.
- 14-day refund, no conditions
- 14-day refund, no conditions: ask within 14 days of buying and you get the whole payment back.
- No pass guarantee. These are practice exams written by us from the exam guide and the Databricks documentation; a score here does not predict your result on the real exam.
Study path
One module per exam domain. Lessons that already exist on BytePatterns are linked; the rest are being written.
Module 1: Design Applications
1Prompts that return a fixed format: structured outputs, JSON schemas and examplesTask 1.1
Partly covered by: Fine-Tuning vs Prompting
2Matching a business requirement to a model taskTask 1.1
Lesson coming
3Chain components: retriever, prompt template, model and output parserTask 1.2
Partly covered by: Retrieval-Augmented Generation
4From a business goal to the pipeline's inputs and outputsTask 1.2
Lesson coming
5Tools for multi-stage reasoning and the order they run inTask 1.3
Partly covered by: The Tool-Use Loop, Agents and Tools
6Agent Bricks offerings, Information Extraction and Genie AgentsTask 1.3
Lesson coming
Module 2: Data Preparation
7Chunk size, overlap and structure-aware chunkingTask 2.1
Partly covered by: Chunking and Reranking, Context Windows
8Cleaning source documents and choosing the sources that hold the answersTask 2.2
Partly covered by: Data Readiness for AI
9Parsing PDFs, HTML, Office files and images; writing chunks to Delta tablesTask 2.3
Lesson coming
10Measuring retrieval: recall@k, precision@k and retrieval judgesTask 2.4
Partly covered by: Evaluating LLMs
11Reranking retrieved resultsTask 2.4
Partly covered by: Chunking and Reranking
Module 3: Application Development
12LangChain, LangGraph and LlamaIndex with MLflow tracingTask 3.1
Partly covered by: Agents and Tools
13Genie Agents as data tools: the Conversation API and MCPTask 3.1
Lesson coming
14Adding key fields, terms and intents to the promptTask 3.2
Partly covered by: Retrieval-Augmented Generation
15Refining a prompt from a baseline to the desired outputTask 3.2
Partly covered by: Fine-Tuning vs Prompting
16Spotting quality and safety issues; guardrailsTask 3.3
Partly covered by: Guardrails, LLM as a Judge
17Evaluation in development and monitoring in productionTask 3.3
Partly covered by: From Pilot to Production
18Choosing an LLM: context, latency, cost, model cards and experiment metricsTask 3.4
Partly covered by: What Is an LLM, Context Windows
19Chunk size from evaluation; embedding context length and dimensionTask 3.5
Partly covered by: Embeddings, Tokenization
Module 4: Assembling and Deploying Applications
20pyfunc chains, signatures, input examples and Unity Catalog registrationTask 4.1
Lesson coming
21AI Search endpoints and indexes: sync, embeddings, filters and SKUsTask 4.2
Partly covered by: Vector Databases, Approximate Neighbours, Cosine Similarity
22Secrets, endpoint permissions and Foundation Model APIsTask 4.3
Lesson coming
23Batch inference with ai_queryTask 4.3
Lesson coming
24Agent memory and structured lookups on LakebaseTask 4.4
Partly covered by: RDS vs DynamoDB
25Databricks-provided, external and custom MCP serversTask 4.4
Partly covered by: The Tool-Use Loop
26Front ends for agents: Databricks Apps, Slack and TeamsTask 4.4
Lesson coming
27CI/CD for agents and versioned promptsTask 4.5
Lesson coming
Module 5: Governance
28Masking sensitive data in sources, prompts and tracesTask 5.1
Partly covered by: Finding and Masking PII
29Guarding against prompt injection and malicious inputTask 5.1
Partly covered by: Guardrails
30Model and data licensesTask 5.2
Lesson coming
31Mitigating problematic text in source dataTask 5.2
Partly covered by: Finding and Masking PII
Module 6: Evaluation and Monitoring
32Choosing model size from evaluation metrics; metrics to monitorTask 6.1
Partly covered by: Evaluating LLMs
33Controlling LLM cost on DatabricksTask 6.1
Partly covered by: Model Routing and Fallbacks
34MLflow evaluation: judges, ground truth and custom scorersTask 6.2
Partly covered by: LLM as a Judge
35SME feedback with the Review App and labeling sessionsTask 6.2
Lesson coming
36Inference tables, usage tables, rate limits and production tracesTask 6.3
Partly covered by: Observability Basics
Every task in the exam guide
The exam guide splits each domain into task statements. Each one has a page with a short summary, the lessons that teach it and open sample questions.
Domain 1: Design Applications
- 1.1Design a prompt that elicits a specifically formatted response; Select model tasks to accomplish a given business requirement7 sample questions
- 1.2Select chain components for a desired model input and output; Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline7 sample questions
- 1.3Define and order tools that gather knowledge or take actions for multi-stage reasoning; Determine how and when to use Agent Bricks (Knowledge Assistant, Multiagent Supervisor, Information Extraction) to solve problems6 sample questions
Domain 2: Data Preparation
- 2.1Apply a chunking strategy for a given document structure and model constraints; Design retrieval systems using advanced chunking strategies7 sample questions
- 2.2Filter extraneous content in source documents that degrades quality of a RAG application; Identify needed source documents that provide necessary knowledge and quality for a given RAG application6 sample questions
- 2.3Choose the appropriate Python package to extract document content from provided source data and format; Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog6 sample questions
- 2.4Use tools and metrics to evaluate retrieval performance; Explain the role of re-ranking in the information retrieval process6 sample questions
Domain 3: Application Development
- 3.1Select Langchain/similar tools for use in a Generative AI application; Utilize MLflow and Agent Framework for developing agentic systems; Enable multi-agent systems to leverage Genie Spaces or conversational API to retrieve data8 sample questions
- 3.2Augment a prompt with additional context from a user's input based on key fields, terms, and intents; Create a prompt that adjusts an LLM's response from a baseline to a desired output7 sample questions
- 3.3Qualitatively assess responses to identify common issues such as quality and safety; Implement LLM guardrails to prevent negative outcomes; Compare the evaluation and monitoring phases of the Gen AI application life cycle8 sample questions
- 3.4Select the best LLM based on the attributes of the application to be developed; Select a model from a model hub or marketplace for a task based on model metadata/model cards; Select the best model for a given task based on common metrics generated in experiments8 sample questions
- 3.5Select chunking strategy based on model & retrieval evaluation; Select an embedding model context length based on source documents, expected queries, and optimization strategy7 sample questions
Domain 4: Assembling and Deploying Applications
- 4.1Code a chain using a pyfunc model with pre- and post-processing; Code a simple chain according to requirements; Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature; Register the model to Unity Catalog using MLflow7 sample questions
- 4.2Create and query a Vector Search index; Explain the key concepts and components of Mosaic AI Vector Search; Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements7 sample questions
- 4.3Control access to resources from model serving endpoints; Identify how to serve an LLM application that leverages Foundation Model APIs; Identify batch inference workloads and apply ai_query() appropriately7 sample questions
- 4.4Configure a persistent datastore to store and retrieve intermediate memory or structured information; Integrate managed, external, and custom MCP servers based on a given application requirements; Develop an appropriate interactive user facing interface for an agent usage scenario (Apps, Slack, Teams, etc.)6 sample questions
- 4.5Apply CI/CD best practices such as updating a Vector Search index, promoting prompts across environments, and testing individual components of an agent; Apply prompt version control and manage prompt lifecycle6 sample questions
Domain 5: Governance
- 5.1Use masking techniques as guard rails to meet a performance objective; Select guardrail techniques to protect against malicious user inputs to a Gen AI application7 sample questions
- 5.2Use legal/licensing requirements for data sources to avoid legal risk; Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application6 sample questions
Domain 6: Evaluation and Monitoring
- 6.1Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics; Select key metrics to monitor for a specific LLM deployment scenario; Use Databricks features to control LLM costs6 sample questions
- 6.2Evaluate agent performance using MLflow scoring and tracing; Identify evaluation judges that require ground truth; Use Databricks custom Scorers for evaluating agents and LLMs; Incorporate SME feedback to improve agent performance7 sample questions
- 6.3Use inference logging to assess deployed RAG application performance; Use inference tables and Agent Monitoring to track a live LLM endpoint; Use AI Gateway (Inference Tables, Usage Tables, and rate limiting) to track an LLM or agent deployed via Agent Framework6 sample questions
How these questions are made
- Written by us from the exam guide's task statements and the Databricks documentation — never from real exam content.
- Every option carries its own explanation and a link to the Databricks page that proves it.
- A question enters a timed exam only after an independent check; until then it is a practice question.
- Results are a plain percentage, per domain too — not an imitation of Databricks's scaled score.
Questions about the Generative AI Engineer Associate practice exam
- Is the Generative AI Engineer Associate practice exam free?
- Yes. The full practice exam (60 questions, 90 minutes) is free with no sign-up, and so are the 148 practice questions, the task pages and the study path. Your attempts are saved in this browser, on this device.
- How many questions does it have, and is it timed?
- 60 questions in 90 minutes: the real Generative AI Engineer Associate pace, scaled to this form. When the time runs out, the exam is submitted as it stands.
- Are the answers explained?
- After you submit, not during: like the real exam, the timed exam gives no feedback until the end. Then you get your score overall and per exam domain, and every question with each option explained and a link to the Databricks documentation page behind it. In practice mode an answer is explained as soon as you check it.
- What does the Generative AI Engineer Associate exam pack add?
- 3 more timed practice exams (Practice exam 1: 60 questions, 90 minutes; Practice exam 2: 60 questions, 90 minutes; Practice exam 3: 60 questions, 90 minutes), each scored and explained the same way, and the next version of this exam if it changes within your 12 months. 12 months of access. One payment, nothing renews by itself. Everything above stays free. See the pack.
- Can I get a refund?
- Yes, with no conditions: ask within 14 days of buying and you get the whole payment back. The details are in the terms.
- Is this an official Databricks practice exam?
- No. BytePatterns is not affiliated with, endorsed or sponsored by Databricks. Every question is written by us from the public exam guide and the Databricks documentation, never from real exam content, and a score here does not predict your result on the real exam. Databricks lists its own preparation resources on its certification page.