Databricks Generative AI Engineer Associate

The Databricks Certified Generative AI Engineer Associate validates the ability to design and implement LLM-enabled solutions on the Databricks platform. It covers the end-to-end lifecycle of building GenAI applications — from problem decomposition and model selection, to RAG pipeline development, agent orchestration, and production deployment using Databricks tools such as Vector Search, Model Serving, MLflow, and Unity Catalog.

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What is the Databricks Generative AI Engineer Associate?

Who Should Take This Course?

ML Engineers and AI Engineers building LLM-powered applications
Data Scientists working on Retrieval-Augmented Generation (RAG) systems
Software Engineers integrating foundation models into production pipelines
AI Architects designing scalable generative AI solutions on Databricks
Data Engineers adding GenAI capabilities to existing Databricks workflows
Professionals seeking to validate hands-on Databricks GenAI expertise

What You Will Learn in the GAI Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Designing Generative AI Applications

Break down complex GenAI requirements and select the right models and tools.

Problem decomposition for LLM-based solutions
Choosing foundation models: open-source vs. proprietary trade-offs
Selecting retrieval strategies: keyword, semantic, and hybrid search
Architecture patterns for RAG and agent-based systems

Data Preparation for GenAI

Prepare and process data for use in GenAI pipelines.

Chunking strategies for documents and unstructured text
Embedding generation with open-source and Databricks-hosted models
Building and managing vector indexes with Databricks Vector Search
Data quality and preprocessing pipelines for RAG

Application Development

Build and orchestrate LLM chains and RAG applications.

Prompt engineering: system prompts, few-shot, and chain-of-thought
Building RAG applications with LangChain and LlamaIndex on Databricks
LLM chains, tool use, and multi-step reasoning agents
Structured output generation and function calling

Assembling and Deploying GenAI Apps

Package, version, and deploy generative AI applications to production.

Logging GenAI applications with MLflow's AI Gateway
Model Serving for real-time LLM inference on Databricks
Deploying RAG pipelines as REST API endpoints
Versioning GenAI applications with MLflow Model Registry

Governance, Evaluation, and Monitoring

Ensure responsible AI practices and maintain production quality.

Unity Catalog for GenAI asset governance and lineage
Evaluating RAG quality: faithfulness, relevancy, and context recall
MLflow LLM Evaluate for automated quality scoring
Monitoring production GenAI apps for drift and quality degradation

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

Python programming proficiency (all exam code is Python)
Familiarity with machine learning concepts and workflows
Basic understanding of NLP and language model fundamentals
6+ months of hands-on experience with GenAI tasks (recommended)
No formal prerequisites — Databricks Academy GenAI training highly recommended

Exam Information

Everything you need to know about the GAI certification exam.

Exam Component	Details
Exam Name	Databricks Certified Generative AI Engineer Associate
Exam Code	GAI
Exam Type	Multiple Choice
Total Questions	45
Passing Score	70%
Exam Duration	90 minutes
Language	English, Japanese, Portuguese (BR), Korean
Exam Provider	Databricks / Kryterion (online proctored or test center)
Exam Focus	LLM application design, RAG pipelines, Vector Search, Model Serving, MLflow, and Unity Catalog governance
Exam Registration	Databricks Academy portal (academy.databricks.com)
Retake Policy	14-day waiting period before retake
Certification Validity	2 years