Option B is correct. The 'SNOWFLAKE.CORTEX.FINETUNE function requires the training data query result to include columns named 'prompt' and 'completion'. Using SELECT ... AS prompt, ... AS completion" aliases the existing columns to the required names. The cost for fine-tuning is based on the number of tokens used in training, specifically calculated as 'Fine-tuning trained tokens = number of input tokens ' number of epochs trainecf. Additionally, running 'AI_COMPLETE on a fine-tuned model incurs compute costs for both input and output tokens processed. Option A is incorrect because the columns need to be aliased to 'prompt' and 'completion', and it misstates the cost calculation for fine-tuning training itself. Option C is incorrect because the syntax for FINETUNE is a SQL function, not a 'CREATE SNOWFLAKE.ML.FINETUNE' command, and Cortex Fine-tuning incurs compute costs based on tokens, not a fixed-rate subscription model. Option D is incorrect because the first argument for the function is 'CREATE', not 'TRAIN', and training costs are based on tokens, not GPU compute hours, although compute resources are utilized. Option E is incorrect because the training data must come from a Snowflake table or view, and costs are incurred for training and inference, not just storage.