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
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 Engineering
How to build RAG (Retrieval-Augmented Generation) applications using LangChain and Mosaic AI Vector Search
Strategies for chunking and preparing data using Delta Lake and Unity Catalog
How to deploy GenAI apps using MLflow, Model Serving, and Inference APIs
Setting up guardrails, masking, and governance to keep your models safe and compliant
How to monitor GenAI pipelines using MLflow metrics, inference logs, and evaluation tools
How to crack the Databricks Generative AI Engineer certification with real-world examples, mapped exam topics, and practice questions
Why This Course?
100% aligned with the official Databricks exam guide
Practical demos, hands-on projects, and real-world case studies
Covers tools like LangChain, MLflow, Vector Search, Unity Catalog, LLM APIs
Includes mock questions and exam preparation tips
No prior GenAI experience needed — beginner-friendly!
