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 theDatabricks Certified Generative AI Engineer Associate Examand take your Generative AI skills to the next level?

This hands on course is designed to help youmaster Databricks tools and frameworksused to build real-world LLM applications  and prepare you thoroughly for the officialDatabricks GenAI certification.

Whether you're adata engineer, ML developer, cloud professional, or AI enthusiast, this course will equip you with theskills and confidenceto design, develop, deploy, and monitor end-to-end LLM-powered apps using Databricks.

What You’ll Learn:

The fundamentals ofGenerative AI,LLMs, andPrompt Engineering

How to buildRAG (Retrieval-Augmented Generation)applications using LangChain and Mosaic AI Vector Search

Strategies forchunking and preparing datausing Delta Lake and Unity Catalog

How to deploy GenAI apps usingMLflow, Model Serving, and Inference APIs

Setting upguardrails, masking, and governanceto keep your models safe and compliant

How tomonitor GenAI pipelinesusing MLflow metrics, inference logs, and evaluation tools

How tocrack the Databricks Generative AI Engineer certificationwith real-world examples, mapped exam topics, and practice questions

Why This Course?

100% aligned with the official Databricks exam guidePractical demos, hands-on projects, and real-world case studiesCovers tools likeLangChain, 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
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