Free Databricks GenAI mock exams

This exam rewards knowing how Databricks wants a generative AI application built, not just how RAG works in general. Many options describe approaches that would work somewhere; the right one uses the Databricks tool built for the job — Vector Search rather than a hand-rolled store, MLflow and Unity Catalog for the model lifecycle, Model Serving and AI Gateway for deployment and control. Generic LLM experience gets you halfway.

Application development carries the most weight, and its questions are trade-offs: which model for this latency and cost, which embedding context length for these documents, which chunking strategy for this retrieval result. Read the stated constraint first — scale, latency, cost or accuracy — because it is what eliminates the otherwise reasonable options.

Vector Search configuration is a frequent source of misses. Know the difference between endpoint types, between indexes that sync from a Delta table and those you write to directly, and what hybrid search and re-ranking change. Questions combine these settings, so one wrong assumption costs the whole answer.

Evaluation and monitoring questions turn on the MLflow tooling: which judges need ground truth, how tracing and custom scorers fit together, and what inference tables and AI Gateway record. Candidates who have only built prototypes, never measured them, find this section harder than its weight suggests.

Some questions ask for two answers. On those, both choices must be right for the mark, so when one option is clearly correct, look harder at the second rather than picking the nearest neighbour.