The task described in the question involves enhancing customer experience by automatically handling common customer inquiries using an LLM-powered AI solution. This requires the system to process input data (customer inquiries) and generate personalized, relevant responses efficiently. Let’s evaluate the options step-by-step in the context of Databricks Generative AI Engineer principles, which emphasize leveraging LLMs for tasks like question answering, summarization, and retrieval-augmented generation (RAG).

Option A: Input: Customer reviews; Output: Group the reviews by users and aggregate per-user average rating, then respond

This option focuses on analyzing customer reviews to compute average ratings per user. While this might be useful for sentiment analysis or user profiling, it does not directly address the goal of handling common customer inquiries or improving response times for personalized interactions. Customer reviews are typically feedback data, not real-time inquiries requiring immediate responses.

Databricks Reference: Databricks documentation on LLMs (e.g., "Building LLM Applications with Databricks") emphasizes that LLMs excel at tasks like question answering and conversational responses, not just aggregation or statistical analysis of reviews.

Option B: Input: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions, then respond

This option uses chat logs as input, which aligns with customer service scenarios. However, the output—grouping by users and summarizing interactions—focuses on user-specific summaries rather than directly addressing inquiries. While summarization is an LLM capability, this approach lacks the specificity of finding answers to common questions, which is central to the problem.

Databricks Reference: Per Databricks’ "Generative AI Cookbook," LLMs can summarize text, but for customer service, the emphasis is on retrieval and response generation (e.g., RAG workflows) rather than user interaction summaries alone.

Option C: Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

This option uses chat logs (real customer inquiries) as input and tasks the LLM with identifying answers to similar questions, then providing a summarized response. This directly aligns with the goal of handling common inquiries efficiently while maintaining personalization (by referencing past interactions or similar cases). It leverages LLM capabilities like semantic search, retrieval, and response generation, which are core to Databricks’ LLM workflows.

Databricks Reference: From Databricks documentation ("Building LLM-Powered Applications," 2023), an exact extract states:"For customer support use cases, LLMs can be used to retrieve relevant answers from historical data like chat logs and generate concise, contextually appropriate responses."This matches Option C’s approach of finding answers and summarizing them.

Option D: Input: Customer reviews; Output: Classify review sentiment

This option focuses on sentiment classification of reviews, which is a valid LLM task but unrelated to handling customer inquiries or improving response times in a conversational context. It’s more suited for feedback analysis than real-time customer service.

Databricks Reference: Databricks’ "Generative AI Engineer Guide" notes that sentiment analysis is a common LLM task, but it’s not highlighted for real-time conversational applications like customer support.

Conclusion: Option C is the best fit because it uses relevant input (chat logs) and defines an LLM task (finding answers and summarizing) that meets the requirements of improving response times and maintaining personalized interaction. This aligns with Databricks’ recommended practices for LLM-powered customer service solutions, such as retrieval-augmented generation (RAG) workflows.