Databricks GenAI Engineer study guide
Databricks GenAI Engineer · Work through your exam, one topic at a time.
The Databricks Certified Generative AI Engineer Associate is a professional credential from Databricks that validates a practitioner's ability to design, build, evaluate, deploy, and govern generative AI applications on the Databricks Data + AI Platform. It is aimed at data engineers, ML engineers, solution architects, and application developers who already work with Spark, Delta Lake, and Unity Catalog and who now need to ship LLM-backed systems - retrieval-augmented generation pipelines, agentic workflows, and evaluation harnesses - rather than train models from scratch. The Databricks GenAI Engineer study guide has 5 sections and about 1,504 words.
01Design and build generative AI applications
A Databricks generative AI application is an endpoint-consuming client plus governed data and models. Know the building blocks: Foundation Model APIs (pay-per-token or provisioned throughput), custom Model Serving endpoints for fine-tuned or packaged models, Vector Search endpoints, and AI Functions such as ai query for SQL inference.
304 words, about 2 min02Prepare data for retrieval augmented generation (RAG)
RAG quality is mostly a data-engineering problem. Ingestion parses files (PDF, HTML, Office), cleans text, and writes to a Delta table with metadata such as source, title, section, timestamp, and access tags.
333 words, about 2 min03Develop, tune and evaluate generative AI applications
Generative AI development is iterative: prompt, evaluate, inspect failures, then change data, prompt, retrieval, or model. Create a small, representative evaluation set with expected answers or rubrics plus edge cases.
283 words, about 1 min04Deploy and monitor generative AI applications
Deploy the application's model or chain to a Model Serving endpoint with autoscaling and concurrency limits. Enable inference tables to capture request and response payloads into a Unity Catalog Delta table; this is the foundation for monitoring, debugging, and quality analysis.
291 words, about 1 min05Governance, security and responsible AI for generative AI applications
Every component, including source documents, Delta tables, Vector Search indexes, models, endpoints, and inference logs, should live in Unity Catalog with owners, tags, and lineage. Grant least privilege: consumers need SELECT or EXECUTE on what they use, not ownership.
293 words, about 1 min