Welcome to the official companion repository for the upcoming O’Reilly book:
"Databricks Certified Generative AI Engineer Associate Study Guide: Generative AI with Databricks"
by "Dr.Monika Arora" — EXL Service, Databricks Gen AI Engineer Associate Certified.
This repository contains the notebooks, code samples, hands-on labs, and practice materials that accompany the book, helping readers prepare for the Databricks Generative AI Engineer Associate Certification.
This study guide provides a comprehensive, exam-aligned learning path for the Databricks Certified Generative AI Engineer Associate exam.
It blends theory, practical labs, and exam preparation to ensure you gain both the knowledge and the hands-on skills to succeed.
The book covers Generative AI, LLMs, RAG workflows, MLflow, Unity Catalog, and Mosaic AI, with labs and quizzes at the end of each chapter, plus a full-length practice exam.
What makes this book unique
- First dedicated guide for the Databricks GenAI Associate Certification
- Includes hands-on Databricks notebooks for practice
- Provides quizzes and a final 20-question practice exam
- Covers not only exam objectives but also real-world GenAI implementations
The repository mirrors the structure of the book, with folders for each chapter, including notebooks (.dbc and .ipynb), quizzes, and datasets.
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Chapter 1: Exam Details and Resources
Understand the certification format, domain weightage, and how to set up your Databricks environment. Includes preparation strategies and a quiz. -
Chapter 2: Designing Generative AI Applications
Learn prompt design, task alignment, and multi-stage reasoning. Hands-on lab: Designing and Evaluating Prompt Chains. -
Chapter 3: Preparing and Chunking Data for RAG Applications
Implement document chunking, content filtering, and retrieval evaluation. Lab: Chunking and Indexing for RAG. -
Chapter 4: Building GenAI Applications with Python and LangChain
Use LangChain for prompts, chains, and guardrails. Lab: Building a Retrieval-Augmented GenAI App. -
Chapter 5: Deploying and Integrating RAG Systems on Databricks
Assemble and deploy end-to-end RAG pipelines. Lab: RAG System Deployment with MLflow & Vector Search. -
Chapter 6: Managing Models with MLflow and Unity Catalog
Track, register, and govern models. Lab: Model Management with MLflow and Unity Catalog. -
Chapter 7: Responsible AI
Apply guardrails, privacy, and compliance measures. Lab: Implementing AI Guardrails. -
Chapter 8: Monitoring & Evaluating LLMs in Production
Track metrics, token usage, latency, and anomalies. Lab: Evaluating and Monitoring LLM Performance. -
Chapter 9: Scaling AI Solutions with Vector Search and Mosaic AI
Build scalable retrieval systems and optimize performance. Lab: Scalable Vector Search with Mosaic AI. -
Chapter 10: Certification Preparation
Review the blueprint, practice strategies, and a 20-question full-length exam. Lab: Certification Readiness Assessment.
To run the labs, you’ll need:
- Databricks Workspace (Community or Enterprise)
- Python 3.9+
- Databricks Runtime ML (latest recommended)
- Libraries from
requirements.txt:pip install -r requirements.txt