Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 154 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 154

Single answer

Your team is developing an AI-powered customer service chatbot for a global e-commerce platform. The chatbot needs to understand and respond in multiple languages, handle domain-specific queries about product details and order status, and operate efficiently in a production environment with high traffic. Which LLM would be the best choice for this application?

  1. A

    A general-purpose open-source model such as GPT-NeoX-20B

  2. B

    A domain-tuned LLM fine-tuned on e-commerce data and multilingual tasks

  3. C

    A small-scale LLM optimized for low-latency responses, but trained only on English data

  4. D

    A proprietary LLM with high accuracy in multilingual tasks but limited to low query throughput

Show answer and explanation

Correct answer: B

Explanation

The best LLM for the application should meet all the requirements: understanding multilingual queries, handling domain-specific data, and operating efficiently in a production environment with high traffic. A domain-tuned LLM that has been fine-tuned on e-commerce data and multilingual tasks satisfies all these criteria, making it the optimal choice for the customer service chatbot.

  • A. Incorrect.

    A general-purpose open-source model like GPT-NeoX-20B may lack the pre-training or fine-tuning required for domain-specific tasks and multilingual support, making it less effective for the chatbot's needs.

  • B. Correct.

    A domain-tuned LLM fine-tuned on e-commerce data and multilingual tasks is the best choice as it is tailored to handle domain-specific queries and multiple languages, while also being efficient for production use.

  • C. Incorrect.

    A small-scale LLM optimized for low-latency responses but trained only on English data would not meet the multilingual requirements of the chatbot, even if it performs well in terms of response speed.

  • D. Incorrect.

    A proprietary LLM with high accuracy in multilingual tasks but limited to low query throughput would not scale well in a high-traffic production environment, making it unsuitable for this use case.

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