Options A and C are the most valid and efficient approaches. Option A provides a structured and readily queryable format for the audit logs, making it easy to analyze and report on fraud detection performance. Using a SEQUENCE ensures unique and ordered log IDs. Option C leverages Snowflake's Streams and Tasks to automate the prediction process and audit logging, ensuring that all transactions are processed and logged in near real-time. This is particularly suitable for continuous fraud detection. Option B is less efficient due to the overhead of loading unstructured data and parsing it. It lacks real-time processing capabilities. Option D introduces external dependencies and potential latency. While external logging services can be valuable, storing the audit data natively in Snowflake provides better integration and performance. Option E is not reliable for recreating the full audit trail, as primarily captures query execution metadata and may not contain all the necessary information (e.g., input features, model version). Also SYSTEM$QUERY LOG data availability can be delayed.