Option B is correct because a Verified Query Repository (VQR) helps Cortex Analyst leverage pre-validated SQL for similar questions, improving accuracy and potentially reducing the number of LLM inference calls or complex reasoning steps required for SQL generation, thus making usage more efficient and reducing cost associated with less optimal LLM calls. Option D is correct because integrating Cortex Search Services improves literal search, helping Cortex Analyst find exact literal values needed for SQL queries more accurately and efficiently, which can reduce ambiguity and the need for multiple LLM iterations or incorrect queries, ultimately leading to more cost-effective message processing. Option A is incorrect: While using a smaller LLM might seem to save cost, Llama 3.1 70B was specifically chosen as the summarization agent for multi-turn conversations in Cortex Analyst due to its higher accuracy in rephrasing questions and avoiding errors, implying that a less capable model would degrade performance and potentially lead to more (and thus more expensive) overall messages to achieve a correct answer. The cost for Cortex Analyst is per message, not per token for this component. Option C is incorrect. While a well- scoped semantic model is recommended for accuracy, the sources do not explicitly state that reducing the number of logical tables and columns 'directly' reduces the per-message cost of Cortex Analyst, which is fixed per message. The impact would be indirect through improved accuracy or reduced processing complexity, but not a direct cost reduction based on metadata size for the fixed per-message billing. Option E is incorrect. Cortex Analyst cost is based on the number of messages, not the token count of prompts. While good prompt engineering (like concise custom instructions) is generally good practice, it does not directly reduce the per-message cost of Cortex Analyst as it would for token- based LLM calls.