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 PyPI. The 'create_service' call correctly specifies and to leverage GPU acceleration, as SPCS supports GPU instances for ML workloads. 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 PyPI in the scenario, meaning it cannot be installed via 'conda-forge' . While 'conda-forge' is the default for SPCS dependencies, not all packages are available there. Option C is incorrect because is chosen, which will not provide GPU acceleration required by the PyTorch model for GPU-powered inference. GPU acceleration requires a GPU compute pool. Option D is incorrect because 'opencv-python' is not available through Anaconda channels (as per the scenario that it is PyPI 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 for specific packages, Snowflake explicitly recommends 'using only 'conda_dependencies' or only 'pip_requirements', not both' for managing dependencies to avoid potential conflicts.