Databricks · Associate
Databricks Generative AI Engineer Associate
Design, build, and deploy LLM-powered applications on Databricks.
Generative AI Engineer Associate exam at a glance
| Exam code | — |
|---|---|
| Vendor | Databricks |
| Level | Associate |
| Domains | 6 |
| Published practice questions | 250 |
| Our mock exam | 45 questions in 90 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | July 6, 2026 |
| Official exam page | Databricks — Generative AI Engineer Associate |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official Generative AI Engineer Associate exam page for the current format, price and scoring before you book.
Generative AI Engineer Associate exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Design applications
14%Design prompts and select model tasks for business requirements.
- Prompt design: zero-shot, few-shot, chain-of-thought
- Selecting model tasks (summarization, extraction, chat) from requirements
- Choosing chain components for multi-stage reasoning
- When to use RAG vs fine-tuning vs prompting
- Defining quality requirements and guardrails up front
Free Design applications practice questions →Design applications study guide →
Data preparation
14%Prepare and chunk data for retrieval-augmented generation.
- Chunking strategies: fixed size, semantic, document-aware
- Document extraction from PDFs and unstructured sources
- Cleaning and filtering source content for retrieval quality
- Delta tables as RAG sources; Vector Search index sync
- Embedding model selection and dimensionality trade-offs
- Evaluating retrieval quality: recall, precision, re-ranking
Free Data preparation practice questions →Data preparation study guide →
Application development
30%Build RAG applications, chains, and agents on Databricks.
- Mosaic AI Vector Search: creating and querying indexes
- LangChain integration patterns on Databricks
- Foundation Model APIs: pay-per-token vs provisioned throughput
- Prompt engineering iteration and guardrail implementation
- Agents and tool calling; function execution
- Context management and conversation memory
- Selecting models by quality, cost, latency (LLM-as-judge comparisons)
- MLflow PyFunc and ChatModel flavors for GenAI apps
Free Application development practice questions →Application development study guide →
Assembling and deploying applications
22%Package, register, and serve GenAI applications.
- MLflow logging and registering chains/agents in Unity Catalog
- Model Serving endpoints: creation, scaling, routes
- Deploying RAG chains behind serving endpoints
- Secrets and credentials in serving (external model access)
- Inference tables for request/response capture
- Version management and rollout strategies
Free Assembling and deploying applications practice questions →Assembling and deploying applications study guide →
Governance
8%Apply security and governance to GenAI systems.
- Guardrails for harmful or off-topic content
- PII handling and masking in prompts and retrieval data
- Unity Catalog permissions for models and indexes
- Legal/licensing considerations for model and data use
- Audit and traceability of AI outputs
Free Governance practice questions →Governance study guide →
Evaluation and monitoring
12%Evaluate and monitor GenAI application quality and cost.
- Mosaic AI Agent Evaluation; LLM-as-judge metrics
- Offline eval sets vs online monitoring
- Inference tables + Lakehouse Monitoring for drift
- Cost and latency tracking per endpoint
- Detecting hallucination and retrieval failures
- Feedback loops and continuous improvement
Free Evaluation and monitoring practice questions →Evaluation and monitoring study guide →
Generative AI Engineer Associate FAQ
How is the Generative AI Engineer Associate practice assessment configured?
Our practice format targets 45 questions in 90 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Check the provider's current exam guide for official scoring. A practice percentage is not an official scaled score. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in Generative AI Engineer Associate?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study Generative AI Engineer Associate?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use Generative AI Engineer Associate exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official Databricks exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.