Databricks Genai Engineer Associate Exam Preparation
 

Databricks Genai Engineer Associate Exam Preparation
Published 7/2026
Created by Aseem Mankotia
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 10 Lectures ( 2h 55m ) | Size: 588.3 MB
Master Databricks Vector Search, MLflow, Model Serving and RAG for the Certified GenAI Engineer exam
What you'll learn
⚡ Design and build production RAG applications with Databricks Vector Search and MLflow
⚡ Develop multi-stage chains and agents with LangChain, tools and function calling on Databricks
⚡ Deploy GenAI apps with Model Serving and Foundation Model APIs, then evaluate with MLflow
⚡ Govern GenAI assets with Unity Catalog, guardrails and PII controls
⚡ Working Python and SQL plus basic ML knowledge; familiarity with LLM and RAG concepts helps
Requirements
❗ Data engineers, ML engineers and AI developers building generative AI applications on Databricks
Description
This course contains the use of artificial intelligence.
Prepare for the Databricks Certified Generative AI Engineer Associate exam with a course built around the six official exam domains at their published weightings, with the heaviest coverage on Application Development (30 percent) and Assembling and Deploying Applications (22 percent) - exactly where the exam concentrates its questions.
This is a hands-on platform course, not a generic GenAI overview. Every concept is grounded in the specific Databricks tools the exam tests: you will design compound AI applications, prepare data and chunking strategies for retrieval, build RAG applications with Databricks Vector Search, construct multi-stage chains and agents with LangChain, log and register them with MLflow, deploy with Model Serving and Foundation Model APIs, govern assets with Unity Catalog and AI guardrails, and evaluate deployed apps with MLflow LLM evaluation and Lakehouse Monitoring. Labs are notebook-style walkthroughs you can follow in a Databricks workspace, from creating a Vector Search index over a Delta table to serving and monitoring a complete RAG chain.
Taught by Aseem Mankotia (TechNuggets Academy). Every chapter includes exam callouts flagging what Databricks actually tests, common traps to avoid, and scenario-based practice questions in the style of the real exam. Two full-length practice tests with 90 scenario questions and detailed explanations round out your preparation, and the final chapter walks through a complete 45-question exam simulation with strategy for the 90-minute format.
Databricks recommends six months of hands-on experience with generative AI solutions before sitting the exam; this course assumes working Python and SQL plus basic ML familiarity.
Who this course is for
⭐ Data engineers, ML engineers and AI developers building generative AI applications on Databricks
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