Option A is incorrect. Cortex Analyst does not simply pass the full, verbatim history of all previous prompts and responses to every subsequent LLM call. This 'primitive way' could lead to longer inference times, more non-determinism, and degraded performance due to multitasking. Instead, it uses an LLM summarization agent to manage context. Option B is correct. Cortex Analyst supports multi-turn conversations by recognizing follow-up questions and using an LLM summarization agent (such as Llama 3.1 70B, which showed high accuracy in this role) to retrieve context from the conversation history and reframe the current-turn question. Option C is correct. The Verified Query Repository (VQR) is a collection of pre-verified questions and corresponding SQL queries that helps improve the accuracy and trustworthiness of Cortex Analyst's results by using relevant SQL queries for similar questions. Option D is incorrect. While semantic search over sample values can improve literal search for Cortex Analyst, it is not the primary mechanism for managing the context of multi-turn conversations. Context management relies on an LLM summarization agent. Option E is correct. Semantic models, captured in lightweight YAML files, are critical for Cortex Analyst. They provide richer semantic information than basic database schemas, bridging the gap between business user language and technical database definitions, which is essential for accurate text-to-SQL conversions in both initial and follow-up queries.