Databricks Certified Generative AI Engineer Associate — Question 56

A Generative AI Engineer is building a RAG application for answering employee questions on company policies.

What are the steps needed to build this RAG application and deploy it?

Answer options

• A. Ingest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving
• B. User submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
• C. Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -> Deploy it using Model Serving -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response
• D. Ingest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving

Correct answer: D

Explanation

The correct answer is D because it follows the logical sequence of first ingesting and indexing documents, allowing user queries to be sent to the LLM, which retrieves relevant information, generates a response, evaluates the model, and finally deploys it. The other options disrupt this workflow by starting with user queries or misplacing the evaluation and deployment steps, which would lead to an incomplete or ineffective application setup.