Databricks Generative AI Engineer Associate Question 207
Select 5You are designing a Retrieval-Augmented Generation (RAG) application to create a chatbot that answers domain-specific queries using a Databricks environment. Which of the following are essential components to build this RAG application?
- A
Embedding model to generate vector representations of text
- B
Retriever to fetch relevant documents from a knowledge base
- C
Model flavor to define the type of generative model being used
- D
A streaming data source for processing real-time data
- E
Input examples to validate and test the application
- F
Dependencies to ensure all required packages and libraries are available
Show answer and explanation
Correct answers: A, B, C, E, F
Explanation
In a RAG application, the essential components include an embedding model for vectorization, a retriever for fetching relevant documents, a model flavor to define the generative model, input examples for validation, and dependencies to ensure the environment is properly configured. A streaming data source, while useful in some applications, is not a basic requirement for building a RAG system.
- A. Correct.
Correct: An embedding model is crucial in a RAG application to convert the input text and knowledge base documents into vectorized representations for similarity matching.
- B. Correct.
Correct: A retriever is a key component that searches the knowledge base and retrieves the most relevant documents based on the input query.
- C. Correct.
Correct: The model flavor specifies the type of generative model being used (e.g., transformer-based models like GPT), which is a core part of the RAG architecture.
- D. Incorrect.
Incorrect: A streaming data source is not an essential component for a basic RAG application. RAG applications typically rely on static or pre-indexed knowledge bases.
- E. Correct.
Correct: Input examples are necessary to test the application and ensure it performs as expected with realistic queries.
- F. Correct.
Correct: Dependencies are required to ensure all libraries and frameworks (e.g., PyTorch, Transformers, LangChain) needed for the RAG application are properly set up.