Databricks Generative AI Engineer Associate Question 210
Select 4You are tasked with building a Retrieval-Augmented Generation (RAG) application using Databricks. Which of the following components are essential for this application to function correctly?
- A
An embedding model to convert input text into vector representations
- B
A retriever to fetch relevant documents based on vector similarity
- C
A cloud storage bucket to store training datasets
- D
Input examples to test the application
- E
A model signature to define the input and output schema of the model
- F
A monitoring dashboard to track model performance
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To build a functional RAG application, you need an embedding model to vectorize text, a retriever to fetch relevant documents, input examples for testing, and a model signature to define the input-output schema. While tools like cloud storage or monitoring dashboards are helpful, they are not core elements for creating a RAG system.
- A. Correct.
An embedding model is required to convert input text into vectors, which is a critical step for finding semantically similar documents in RAG systems.
- B. Correct.
A retriever is a core component of a RAG application as it fetches relevant documents or data based on the query's vector representation.
- C. Incorrect.
While cloud storage buckets are useful for many machine learning tasks, they are not a fundamental requirement for building a RAG application.
- D. Correct.
Input examples are important for testing and validating the functionality of the RAG system, ensuring it responds accurately to queries.
- E. Correct.
A model signature is essential for defining the input and output schema, ensuring the RAG application integrates correctly with other components.
- F. Incorrect.
A monitoring dashboard can be useful for tracking performance but is not mandatory for the basic functioning of a RAG application.