Databricks Generative AI Engineer Associate: what to actually study

The Databricks Certified Generative AI Engineer Associate exam is less about LLM theory and more about one question asked many ways: given this requirement, which Databricks piece do you reach for, and in what order? If you build RAG apps on Databricks already, most of it will feel familiar. If you don't, these are the areas worth your time.

Always check the current exam guide on the Databricks site for the official domain list and weights. What follows is a sensible way to organise the study.

1. Design: break the problem down first

Expect scenarios that describe a business goal and ask you to pick the right shape of solution:

• Does this need RAG (answers grounded in company documents) or is a well-written prompt enough?
• Is it a single LLM call, a chain of steps, or an agent that decides which tool to call?
• What are the inputs and outputs of each stage? Many questions are really "put these steps in the right order".

A good habit: for any scenario, sketch the pipeline as boxes (ingest, chunk, embed, index, retrieve, generate, evaluate) and ask which box the question is about.

2. Data preparation: chunking is the big one

• Chunking strategy. Fixed-size chunks with overlap, versus splitting on document structure (headings, sections, paragraphs). Know the trade-off: chunks too large dilute retrieval; chunks too small lose context.
• Cleaning. Strip boilerplate (headers, footers, navigation) before chunking, or it pollutes every chunk.
• Storage. Source documents and chunks live in Delta tables governed by Unity Catalog.
• Embedding model choice. The query and the documents must be embedded with the same model, and the model's context length limits your chunk size.

3. Vector Search

This is where a lot of Databricks-specific detail lives:

• Delta Sync index keeps itself in step with a source Delta table. The source table needs Change Data Feed enabled.
• Direct Vector Access index is for when you manage the vectors yourself and write them through the API.
• Embeddings can be computed by Databricks from a text column (managed embeddings) or supplied by you.
• Know what a vector search endpoint is and that indexes are served from it.

4. Building and deploying the app

• MLflow is the packaging layer: log the chain or agent as a model, register it in Unity Catalog, then serve it.
• Model Serving endpoints host both your app and the foundation models (pay-per-token Foundation Model APIs, provisioned throughput, or external models).
• The Mosaic AI Agent Framework is the Databricks way to build, log and deploy agents and RAG apps, and the Review App collects feedback from human reviewers.
• LangChain integration comes up, so be comfortable reading a short chain definition.

5. Evaluation and monitoring

• Offline evaluation before deployment: an evaluation set of questions with expected answers, plus LLM-as-a-judge metrics (groundedness, relevance, correctness, safety). Mosaic AI Agent Evaluation and mlflow.evaluate are the tools to know.
• Retrieval versus generation. If the answer is wrong, was the right chunk never retrieved, or did the model ignore it? Many questions hinge on this distinction.
• Online monitoring through inference tables, which log the requests and responses of a serving endpoint to a Delta table you can analyse.

6. Governance and safety

• Unity Catalog for permissions on data, indexes and models.
• Guardrails on inputs and outputs, for example a safety model such as Llama Guard, or filtering PII before it reaches the prompt.
• Licensing of models and data: know how to check whether a model or dataset can be used commercially before you build on it.

A study order that works

1. Build one small RAG app end to end in a Databricks workspace: Delta table, Vector Search index, a chain, MLflow logging, a serving endpoint. One afternoon of this is worth a week of reading.
2. Read the exam guide and map each objective to the step of your app it belongs to.
3. Do practice questions, and for every wrong answer write down which box of the pipeline it was about. Your weak boxes are your revision list.

Disclosure: we publish practice questions for this exam at AIExamCerts. If you want to test yourself, there's a free practice test for the Generative AI Engineer Associate with explanations.