Crack Databricks Generative AI Engineer Associate Exam

Master RAG, LangChain, Vector Search & MLflow to Build GenAI Apps and Pass the Databricks Certification

What you'll learn
Confidently crack the Databricks Generative AI Engineer Associate Certification with mock questions and scenario-based practice.
Design end-to-end Generative AI applications using Large Language Models (LLMs) with Databricks
Craft effective prompts using real-world frameworks (SALT, RTF, CTF, CoT) to optimize LLM responses.
Build RAG (Retrieval-Augmented Generation) pipelines using tools like LangChain, LlamaIndex, and Mosaic AI Vector Search.
Prepare high-quality data by extracting, chunking, and storing it in Delta Lake with Unity Catalog for scalable LLM use.
Choose and integrate the right models (LLMs, embeddings, tools) based on task, cost, latency, and context window.
Implement safety guardrails and data governance using prompt sanitization, masking, and Unity Catalog.
Deploy and monitor LLM apps with MLflow, Model Serving, and inference tracking tools in Databricks.
Evaluate LLM performance with the right metrics and monitoring strategies to optimize accuracy and cost-efficiency.
Master Databricks-native tools like Vector Search, Model Registry, Unity Catalog, and AI Functions

Requirements
Basic Python programming (functions, dictionaries, loops, APIs)
Familiarity with machine learning concepts (optional, but helpful)
A Databricks Community Edition or enterprise account

Description
Are you ready to crack the Databricks Certified Generative AI Engineer Associate Exam and take your Generative AI skills to the next level?This hands on course is designed to help you master Databricks tools and frameworks used to build real-world LLM applications  and prepare you thoroughly for the official Databricks GenAI certification.Whether you're a data engineer, ML developer, cloud professional, or AI enthusiast, this course will equip you with the skills and confidence to design, develop, deploy, and monitor end-to-end LLM-powered apps using Databricks.What You’ll Learn:The fundamentals of Generative AI, LLMs, and Prompt EngineeringHow to build RAG (Retrieval-Augmented Generation) applications using LangChain and Mosaic AI Vector SearchStrategies for chunking and preparing data using Delta Lake and Unity CatalogHow to deploy GenAI apps using MLflow, Model Serving, and Inference APIsSetting up guardrails, masking, and governance to keep your models safe and compliantHow to monitor GenAI pipelines using MLflow metrics, inference logs, and evaluation toolsHow to crack the Databricks Generative AI Engineer certification with real-world examples, mapped exam topics, and practice questionsWhy This Course?100% aligned with the official Databricks exam guidePractical demos, hands-on projects, and real-world case studiesCovers tools like LangChain, MLflow, Vector Search, Unity Catalog, LLM APIsIncludes mock questions and exam preparation tipsNo prior GenAI experience needed — beginner-friendly!

Who this course is for:
Data Engineers & Data Scientists who want to build and deploy GenAI apps using Databricks, AI/ML Engineers looking to master RAG pipelines, model serving, and evaluation techniques, Analysts & Developers eager to integrate LLMs and prompt engineering into real-world workflows, Students or Career Changers aiming to break into the GenAI space with a hands-on, industry-focused certification, Tech Professionals preparing for the Databricks Generative AI Engineer Associate exam, Teams & Managers evaluating how to bring GenAI capabilities into their Databricks ecosystem, Software Developers and Engineers looking for switch in Generative AI domain