Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 98 of 306

Databricks Certified Generative AI Engineer Associate. Free level, Databricks. Free question with the correct answer and a full explanation.

Databricks Generative AI Engineer Associate Question 98

Select 3

You are tasked with building a knowledge-based Generative AI application that answers customer queries by retrieving relevant information from a large collection of documents and generating natural language responses. Which tool or capability would you prioritize using from LangChain or similar frameworks to efficiently implement this solution?

  1. A

    Document loaders for structured and unstructured data ingestion

  2. B

    Retrieval-Augmented Generation (RAG) pipeline

  3. C

    Pre-trained vision models for image captioning

  4. D

    Memory management for chat history persistence

  5. E

    Data visualization tools for model interpretability

Show answer and explanation

Correct answers: A, B, D

Explanation

Building a knowledge-based Generative AI application involves retrieving information from a collection of documents and generating meaningful responses. LangChain and similar frameworks provide critical features such as document loaders for data ingestion, Retrieval-Augmented Generation (RAG) pipelines for combining retrieval and generation, and memory management for maintaining conversational context. These tools directly address the requirements of the scenario, while other options like image captioning or data visualization are not directly relevant.

  • A. Correct.

    Document loaders are essential for ingesting and preprocessing structured or unstructured data, which is a key step for building a knowledge-based system.

  • B. Correct.

    The Retrieval-Augmented Generation (RAG) pipeline is a critical feature for retrieving relevant information from documents and combining it with generative AI to answer customer queries.

  • C. Incorrect.

    Pre-trained vision models for image captioning are not relevant in this scenario since the task focuses on text-based document retrieval and generation, not image processing.

  • D. Correct.

    Memory management is important for maintaining conversational context, which enhances the user experience in a Generative AI application that involves query-based interactions.

  • E. Incorrect.

    Data visualization tools for model interpretability are beneficial for debugging and analysis but are not directly necessary for implementing a knowledge-based Generative AI application.

Timed practice exam

Take a Databricks Generative AI Engineer Associate practice test under exam conditions

45 questions in 90 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam