Option A is correct. For the
llama3.1-8b
model, the context window specifically allotted for the
prompt
during fine-tuning is 20,000 tokens, and for the
completion
is 4,000 tokens. Option B is correct. The compute cost incurred for Cortex Fine-tuning is based on the number of tokens used in training, which is calculated as 'number of input tokens number of epochs trained'. Option C is incorrect. While max_epochs can be set to a value from 1 to 10 (inclusive), the default is automatically determined by the system. Setting it to the maximum for 'optimal cost efficiency' is not universally recommended, as a higher number of epochs directly increases the compute cost, and the goal is often to select the smallest model that satisfies the need. Option D is incorrect. When using the COMPLETE function for inference with a fine-tuned model, *both* input and output tokens incur compute cost. Option E is correct. Snowpark-optimized warehouses are recommended for Snowpark workloads with large memory requirements, such as ML training use cases, particularly if the training data has more than 5 million rows. Fine-tuning is an ML training process, so this guidance applies.