Databricks Certified Generative AI Engineer - Associate
Question 19
You are deploying a Retrieval-Augmented Generation (RAG) application on Databricks. This application must allow users to submit queries that are embedded into vector space, retrieve the most relevant documents using a retriever, and then pass them to a generative model for response generation. In order to deploy this application, you must ensure that all necessary elements, including dependencies and model signature, are properly specified for a seamless integration into Databricks and for future use by other teams. Which of the following lists the essential components required to deploy this RAG application?
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Explanation:
This option includes all the essential components required for deploying a Retrieval-Augmented Generation (RAG) application effectively:
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Embedding Model: This is necessary for converting user queries and documents into vector representations, enabling semantic search.
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Retriever: This component retrieves the most relevant documents based on the embedded queries, critical for the RAG architecture.
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Generative Model: After retrieving the relevant documents, this model generates responses based on the retrieved information.
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Dependencies: This includes all necessary libraries and packages required for the application to function correctly.
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Model Signature: Specifies the expected inputs and outputs of the model, facilitating integration and ensuring compatibility with other systems.
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Input Examples: Providing example inputs helps with testing and validating the application during deployment and future usage.
Other Options:
- A. Pre-trained language model, document retriever, tokenizer, SQL query generator, dependencies, and input pipeline.
- B. Language model, input format parser, retriever, output formatter, embedding index, and model signature.
- D. Retriever, vectorizer, generative model, dataset schema, hyperparameter configuration, and API gateway.