Databricks Certified Generative AI Engineer Associate

Published weight 14%. Translating business requirements into LLM application designs: choosing components, tasks, models, and multi-stage reasoning / RAG flows.

• Translate requirements into an LLM application design (components, tasks, model selection)
• Design RAG and multi-stage reasoning application flows

Published weight 14%. Preparing data for retrieval: extraction, chunking strategy, embeddings, and building/managing Databricks Vector Search indexes.

• Prepare and chunk data for retrieval (extraction, chunking, embeddings)
• Build and manage vector search indexes (Databricks Vector Search)

Published weight 30%. Developing RAG applications (retrievers, prompts, LangChain chains), prompt engineering and controlling LLM outputs, and building tools/agents and multi-stage chains.

• Develop RAG applications (retrievers, prompts, chains)
• Apply prompt engineering and control LLM outputs
• Build tools, agents, and multi-stage LLM chains

Published weight 22%. Registering and deploying models with MLflow and Databricks Model Serving, and assembling and serving end-to-end generative AI applications and endpoints.

• Register and deploy models with MLflow and Model Serving
• Assemble and serve end-to-end GenAI applications and endpoints

Published weight 8%. Applying governance with Unity Catalog and guardrails: data and model governance, safety/guardrails, and legal and licensing considerations.

• Apply governance with Unity Catalog and guardrails (safety, legal, licensing)

Published weight 12%. Evaluating LLM applications (metrics, LLM-as-judge, MLflow evaluate) and monitoring deployed applications (inference tables, drift, feedback).

• Evaluate LLM applications (metrics, LLM-as-judge, MLflow evaluate)
• Monitor deployed GenAI applications (inference tables, drift, feedback)