DBX-GENAI Databricks Generative AI Engineer Associate

What's inside

The Generative AI Engineer exam isn't about remembering APIs. It's about knowing which Databricks feature fits an application, why, and what breaks when you pick the tempting alternative.

This independent study guide for the Databricks Certified Generative AI Engineer Associate exam turns the exam outline into the decisions, code patterns and traps that building RAG applications and agents on Databricks depends on, written from Databricks' own documentation. Databricks has renamed or retired much of this area since the exam guide was written, so the book maps each name in the guide to its current name and status.

Fifteen chapters, every exam section

Each chapter can be read on its own, and together they cover all six sections of the exam:

• Design: turning a business need into model tasks and chain components, prompts, agent tools and Agent Bricks.
• Prepare data: source documents, ai_parse_document, chunking strategies and writing chunks to Delta tables in Unity Catalog.
• Retrieve: AI Search (Vector Search) endpoints and indexes, hybrid search, re-ranking and retrieval metrics.
• Build and serve: selecting models, chains and pyfunc models, custom agents, Genie, MCP, memory, MLflow, Model Serving and ai_query().
• Ship and govern: prompt versions, CI/CD, user interfaces, guardrails, masking and licensing.
• Evaluate and watch: MLflow scorers and judges, SME feedback, inference tables, production monitoring, Unity Gateway (AI Gateway) and cost control.

Inside

• Comparison tables that set out what decides between similar options, such as a standard or storage-optimized endpoint.
• 170+ Common trap callouts that point out the choices that seem right but aren't, including old names and retired features.
• 18 diagrams, from the stages of a RAG chain to which judges need ground truth.
• Short code examples in Python and SQL, and Key takeaways closing every chapter.
• A glossary of 300+ terms, most linked to the Databricks documentation, with a free online version for readers of the print edition.

Understanding, not memorising

There are no exam questions in this book. It explains the knowledge the exam looks for, so you can reason through scenarios you have never seen and bring the same judgement to a real application.

Written by Tony Rough, a cloud architect with over twenty years in IT infrastructure, who designs data platforms for the organisations he works with.

Part of the Ultra Transcenders series from Distilled Press. An independent publication, not affiliated with, sponsored by or endorsed by Databricks, Inc. Databricks, Delta Lake and Unity Catalog are trademarks of Databricks, Inc. or their respective owners.

Is this book for you?

• Yes, if you're preparing for DBX-GENAI and want to understand why the right answer is right, or you build data pipelines on Databricks and want a compact reference organised by technology.
• It isn't a question bank. The book contains no exam questions. Use it alongside Databricks's free official DBX-GENAI exam guide, and some hands-on time in Databricks Free Edition.
• Leads to Databricks Certified Generative AI Engineer Associate.

Which certification, which book?

Exam coverage

Every skill area in Databricks's DBX-GENAI outline (as of March 18, 2026), and the chapters that cover it.

See every objective and where the book covers it

Chapters

1. From business need to LLM solution
2. Prompt design and refinement
3. Designing agents and using Agent Bricks
4. Finding and extracting source documents
5. Chunking strategies and embedding context length
6. Writing chunks to Unity Catalog and building an index
7. Tuning retrieval: configuration, metrics and re-ranking
8. Selecting models
9. Coding chains and pyfunc models
10. Agent systems: Agent Framework, Genie, MCP and memory
11. Logging, registering and serving
12. Prompts, CI/CD and user interfaces
13. Guardrails and legal risk
14. Evaluating agents with MLflow
15. Monitoring and controlling cost in production

Plus an appendix glossary of 300+ terms, each linked to the Databricks documentation, with the same terms explained free online for print readers.

Sample diagrams

Free study notes

Some sections of the book, free to read online:

• Matching a chunking method to document structure

  Which chunking method suits which kind of document.

• AI Search endpoints: standard or storage-optimized?

  What decides between the two endpoint types for an index.

• The role of re-ranking in retrieval

  What a reranker adds to retrieval, and what it costs.

• LangChain, LangGraph, DSPy or a plain client?

  How to choose a framework for a generative AI application.

• MLflow signatures and input examples

  What signatures and input examples do when logging a model.

• Batch inference with ai_query()

  Which workloads suit ai_query(), and how to run them.

• Which MLflow judges need ground truth?

  The built-in judges that need expected answers, and those that don't.

• Evaluation and monitoring phases compared

  How the evaluation and monitoring phases of a generative AI application differ.

Study plan

A free week-by-week plan through the book at a 4-week or 8-week pace, with the study notes and exam skill areas for each week.

Open the DBX-GENAI study plan