Option A is correct. The 'pip_requirementS argument can be used to specify all necessary Python packages, including 'torch 'transformers' , and 'opencv-python' , which are commonly available on PyPl. The 'create_service' call correctly specifies and to leverage GPU acceleration. This approach aligns with the Snowflake recommendation to use either 'conda_dependencieS or 'pip_requirementS , but not both, for dependency management. Option B is incorrect because 'opencv-python' is specified as only available via PyPl in the scenario, meaning it cannot be installed via 'conda-forge' . Option C is incorrect because is chosen, which will not provide GPU acceleration required by the model. Option D is incorrect because 'opencv-python' is not available through Anaconda channels (as per the scenario that it is PyPl only), and for other conda packages, explicitly specifying the 'conda-forge' channel (e.g., is the recommended practice for SPCS dependencies if they are not in the Snowflake Anaconda channel. Option E is incorrect because, while it correctly separates conda and pip dependencies, Snowflake explicitly recommends 'using only 'conda_dependencieS or only 'pip_requirementss , not both' for managing dependencies to avoid potential conflicts.