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Databricks Generative AI Engineer Associate Study Guide

Databricks Certified Generative AI Engineer Associate

The Databricks Certified Generative AI Engineer Associate exam is for engineers who design and build LLM-enabled solutions on Databricks. It covers designing prompts, chains and agents, preparing and chunking data for retrieval, developing RAG applications and agents, assembling and deploying them with MLflow, Vector Search and Model Serving, governance and guardrails, and evaluating and monitoring them in production.

  • Databricks certification
  • Associate level
  • 6 exam sections

This guide is free, with no sign-up. It explains every Databricks Generative AI Engineer Associate exam topic in plain English, one chapter at a time. Read them in order or jump straight to the topic you need.

  • 6chapters
  • 15topics
  • 5diagrams and exhibits
  • 1 h 16 minfast track
Start with Design Applications
  • Every topic follows the same simple pattern: what it is, how it works, common mistakes
  • Diagrams and real command output where a picture explains it best
  • Exam tips and worked subnetting and IPv6 practice, with answers

How the exam is weighted

Each domain is worth this share of the exam, so spend your study time in proportion. Select a domain to open its chapter.

Chapters

Open a chapter to see its topics and how long each takes.

1Design Applications2 topics · 11 min fast trackPrompts for formatted output, model tasks, chain components, tool order and when to use Agent Bricks.
  1. 1Prompts, Model Tasks and Chain Components5 min
  2. 2Tools, Multi-Stage Reasoning and Agent Bricks6 min
Read the chapter overview →
2Data Preparation2 topics · 10 min fast trackChoosing sources, parsing and cleaning documents, chunking, Delta tables, retrieval metrics, re-ranking.
  1. 1Source Documents, Extraction and Delta Tables5 min
  2. 2Chunking, Retrieval Evaluation and Re-ranking5 min
Read the chapter overview →
3Application Development4 topics · 20 min fast trackModel choice, chunking, prompts, guardrails, agents with MLflow and Genie tools.
  1. 1Selecting LLMs and Models5 min
  2. 2Chunking Strategy and Embedding Context Length5 min
  3. 3Prompts, Context Augmentation and Guardrails5 min
  4. 4Agent Frameworks, MLflow and Genie in Multi Agent Systems5 min
Read the chapter overview →
4Assembling and Deploying Applications4 topics · 20 min fast trackPyfunc chains, Unity Catalog models, Vector Search, serving, ai_query and MCP.
  1. 1Chains, PyFunc Models and Unity Catalog Registration5 min
  2. 2Vector Search Indexes and Endpoints5 min
  3. 3Serving, Access Control, Batch Inference and Interfaces5 min
  4. 4MCP Servers, Memory, Prompt Versions and CI/CD5 min
Read the chapter overview →
5Governance1 topic · 5 min fast trackMasking as a guardrail, defenses against malicious input, source licensing and problem text fixes.
  1. 1Masking, Input Guardrails, Licensing and Source Mitigation5 min
Read the chapter overview →
6Evaluation and Monitoring2 topics · 10 min fast trackMLflow scorers, judges, SME feedback, inference tables, AI Gateway and LLM cost control.
  1. 1Evaluating Agents with MLflow Scorers and SME Feedback5 min
  2. 2Monitoring Live Endpoints and Controlling Costs5 min
Read the chapter overview →

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