Databricks GenAI Engineer Practice Tests: Build Reliable RAG

About this course

This course contains the use of artificial intelligence.

Moving an LLM demonstration into production requires you to check evidence, contracts and release conditions. These Databricks Certified Generative AI Engineer Associate practice tests help you decide whether the application retrieves the right information, returns the required outcome and can be operated responsibly.

Practise task and model selection, source preparation, chunking, retrieval, reranking and evaluation. Application questions connect MLflow, governed models, Agent Bricks, MCP tools, serving and persistent state. Other scenarios distinguish groundedness from correctness and production telemetry from evidence that a release is ready. A schema-valid record, for example, can still omit the fields required for a refusal or success outcome.

The 360 questions form four focused sets and two 45-question, 90-minute mocks. Every option has feedback, with an explanation of the deciding conditions and a study suggestion. Work through the relevant focused area, trace the missing source or prerequisite, then use a mock to test the reasoning under changed constraints.

The scope is the guide effective March 18, 2026. Exam terminology such as Vector Search is distinguished from current AI Search naming, and relevant legacy serving assumptions are stated. Python, Databricks and basic LLM/RAG familiarity are recommended; no paid lab is needed to answer the questions.

This independent resource supplies written practice, not a hosted workspace or official exam items. The 80% revision goal is author-set, and additional unscored exam items are not modelled. Practice results do not guarantee certification.

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