☁️ Databricks Generative AI Engineer Associate - Exam-Prep

For data engineers, ML practitioners, and developers building production GenAI systems on Databricks who want to pass the Generative AI Engineer Associate exam and design, build, deploy, govern, and monitor RAG and LLM applications with confidence. The course is organized into 8 modules, ending with a final exam (pass mark 70%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• Designing GenAI Applications: Prompts, Model Tasks & Chain Components
• Data Preparation: Chunking, Embeddings & Vector Stores for RAG
• Application Development I: Chains, Retrievers & Prompt Engineering
• Application Development II: LangChain, Tools & Agentic Workflows on Databricks
• Assembling & Deploying Applications: MLflow & Model Serving
• Governance: Security, Legal, Licensing & Unity Catalog Guardrails
• Evaluation & Monitoring: Metrics, LLM-as-a-Judge & Inference Tables
• Full Mock Exam, Domain-Weighted Review & Exam-Day Strategy

Learning objectives

• Design GenAI application architectures and translate business goals into technical pipeline specifications, model tasks, and multi-stage tool chains.
• Engineer prompts that elicit specifically formatted, task-appropriate responses for retrieval-augmented and agentic workflows.
• Prepare and chunk data, build embeddings, and populate vector stores for high-quality Retrieval-Augmented Generation (RAG).
• Develop GenAI applications using LangChain, chains, retrievers, and the Mosaic AI / Foundation Model APIs.
• Assemble and deploy applications with MLflow, Model Serving endpoints, and the Databricks production stack.
• Apply governance, security, legal, and licensing guardrails to GenAI solutions using Unity Catalog.
• Evaluate and monitor LLM applications with metrics, LLM-as-a-judge, inference tables, and feedback loops.
• Sit a full 45-question, 90-minute mock exam mapped to the official domain weightings and review every answer.