Databricks Generative AI Engineer Associate Question 208
Select 5You are tasked with building a Retrieval-Augmented Generation (RAG) application on Databricks. Which of the following elements are essential to create the application?
- A
Embedding model to convert text into vector representations
- B
Retriever to fetch relevant documents or data based on queries
- C
A deployment environment with specific hardware requirements like GPUs
- D
Dependencies such as libraries and frameworks required to run the application
- E
Input examples to validate and test the RAG application's functionality
- F
Model signature to define the input and output schema of the model
Show answer and explanation
Correct answers: A, B, D, E, F
Explanation
To build a RAG application, core components include an embedding model to encode data, a retriever to fetch relevant information, dependencies to enable the application's functionality, input examples for validation, and a model signature for integration and schema definition. While a specific deployment environment may be useful for operationalization, it is not a fundamental element for creating the application itself.
- A. Correct.
Correct. An embedding model is crucial for converting data into vector representations, which are used for similarity searches in the RAG pipeline.
- B. Correct.
Correct. A retriever is needed to efficiently fetch relevant documents or information from the knowledge base to support responses.
- C. Incorrect.
Incorrect. While a deployment environment may be important for operationalization, it is not a core element required to create the RAG application itself.
- D. Correct.
Correct. Dependencies like libraries (e.g., Hugging Face, LangChain) and frameworks are necessary for the application to function.
- E. Correct.
Correct. Input examples are used to validate and test the RAG pipeline, ensuring it performs as expected during development.
- F. Correct.
Correct. A model signature defines the input and output schema of the model, which is critical for integrating the model into the RAG workflow.