For a multi-turn conversational AI application built with Streamlit, maintaining the conversation history is fundamental. Streamlit's st.session_state' is the primary way to store and manage state across reruns of the application, which is crucial for remembering past interactions in a chat interface. The typical approach involves initializing 'st.session_state.messages' to an empty list and appending messages for each turn. Option A is incorrect because is a database role specific to Document AI, not general Cortex LLM functions. Option C is not a fundamental step for running a Streamlit application in Snowflake (SiS) itself, as SiS directly hosts the Streamlit app; while models can be served via SPCS, the application itself doesn't inherently require it for basic operation. Option D is related to cross-region inference for LLM functions, which controls where inference requests are processed, not a fundamental step for local execution or conversational state management. Option E suggests a configuration ("ON ERROR':'SKIP") that is primarily used with Snowflake ML functions like Anomaly Detection and Time-Series Forecasting to prevent overall training failure for individual series, and is not a direct option for handling errors in 'TRY_COMPLETE in this manner; 'TRY_COMPLETE itself returns NULL on error.