Databricks Generative AI Engineer Associate Question 209
Select 4You are tasked with building a Retrieval-Augmented Generation (RAG) application in Databricks. Which of the following components are essential to successfully create and deploy this application?
- A
Embedding model
- B
Retriever
- C
Model signature
- D
Input examples
- E
Data visualization tools
- F
Model flavor
Show answer and explanation
Correct answers: A, B, C, F
Explanation
To successfully create a RAG application, you need an embedding model to generate vector representations, a retriever for querying the vectorized data, a model signature to define the input/output schema, and a model flavor to specify the type of model being utilized. Input examples and data visualization tools, while helpful in other contexts, are not core components of a RAG system.
- A. Correct.
An embedding model is critical in a RAG application as it is used to convert data into vector representations that can be efficiently retrieved.
- B. Correct.
A retriever is necessary to fetch relevant information from the vector store based on the input query, which is a key function of a RAG system.
- C. Correct.
The model signature defines the input and output schema of the model, ensuring compatibility and correct integration with the rest of the application.
- D. Incorrect.
Input examples, while useful for testing or debugging, are not essential for the creation or deployment of a RAG application.
- E. Incorrect.
Data visualization tools are unrelated to the core components of a RAG application and are not required for its functionality.
- F. Correct.
The model flavor specifies the type of model (e.g., LLM, embedding model) being used, which is crucial for configuring the RAG application.