Option A is correct. For the most consistent results from
COMPLETE
(which is used for inference with fine-tuned models), it is explicitly recommended to set the temperature option to 0, irrespective of the task or model used. Option B is incorrect. Fine-tuning jobs are designed as long-running processes that are tied to a specific worksheet session, allowing users to check their status independently after initiation. Option C is correct. The FINETUNE function has specific regional availability, meaning that the creation of fine-tuning jobs is restricted to accounts in those supported regions. Cross-region inference primarily applies to COMPLETE function calls for LLM inference, not the fine-tuning training process itself. Option D is incorrect. Snowflake Cortex functions, including those for fine-tuning, do not support dynamic tables. Furthermore, dynamic tables have limitations regarding non-deterministic code and stored procedures, which are often involved in complex AI pipelines. Option E is correct. Fine-tuning jobs are long-running, and their status and progress can be monitored by calling the FINETUNE function with the 'DESCRIBE' argument, providing the generated job ID.