Certified Generative AI Engineer AssociateDemo

By Databricks · Question Mode

QuestionQ87

Assembling and Deploying Applications

A Generative AI Engineer is building a RAG application to answer users' questions about technical regulations as they learn a new sport.

What steps are required to build and deploy this RAG application?

  • 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
Explanation

A RAG application prepares its knowledge base by ingesting and indexing documents in vector search. A user query retrieves relevant context, which is supplied to the LLM to generate a grounded response. The application can then be evaluated for quality before it is deployed with Model Serving.

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