Option B is correct. Cortex Analyst introduces an additional LLM summarization agent before the original workflow to handle multi-turn conversations. This agent distills the conversation history, and Llama 3.1 70B was found to be highly effective for this task with a low error rate (96.5% rated as good), even considering the latency-performance tradeoff. Option A is incorrect. Directly passing the entire raw conversation history to every LLM call can lead to longer inference times, more non-determinism, and degraded overall performance due to multitasking. Option C is incorrect. While previous context is used, Cortex Analyst's multi-turn support involves an LLM summarization agent to rewrite the current question based on conversation history, not just reuse cached SQL query results. Option D is incorrect. While there is a latency- performance tradeoff, Llama 3.1 8B showed an approximate 5% error rate in conversation history rewriting, making Llama 3.1 70B the preferred choice for its higher accuracy despite being a larger model. The goal is to select the smallest model that 'satisfies the need', which in this case prioritizes summarization quality. Option E is incorrect. Summarization is a natural language task (rewriting the question based on context), distinct from SQL query generation, which is handled by other agents in the Cortex Analyst workflow.