Databricks Certified Generative AI Engineer Associate — Question 10

A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
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. 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
• C. Ingest documents from a source –> Index the documents and save to Vector Search –> Evaluate model –> Deploy it using Model Serving
• D. 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

Correct answer: B

Explanation

The correct answer, B, outlines the complete process, starting from ingesting documents to deploying the model, ensuring that all necessary steps like indexing, querying, and response generation are included. Option A incorrectly places the evaluation step after response generation, which is not logical for a feedback loop. Option C lacks the querying and response generation steps entirely, making it incomplete. Option D misorders the steps, starting with user queries before necessary document ingestion and indexing.