Option A is correct. When bringing an unsupported model type, such as PyCaret, you must define a 'ModelContext' that refers to the serialized model file (e.g., a pickled file). Option B is incorrect. For models deployed to Snowpark Container Services, 'conda_dependencies' are, by default, obtained from 'conda-forge' , not the Snowflake Anaconda channel, which is used for warehouse deployments. Therefore, relying on the Snowflake Anaconda channel for SPCS deployment is incorrect. Option C is correct. While 'conda_dependencies' can be used for SPCS (resolved from 'conda-forge'), 'pip_requirementS are often a more direct and reliable way to specify dependencies for custom or less common third-party Python packages, ensuring they are pulled directly from PyPI if not available in 'conda-forge' . The PyCaret example in the sources, while using 'conda_dependencies' , represents a specific case, and for broader 'custom third-party packages', pip is a strong choice. Option D is correct. To make the deployed service accessible via an HTTP endpoint, must be set to 'True'. Additionally, 'gpu_requests = (or the appropriate number of GPUs) is essential when deploying a model to a GPU compute pool to ensure it leverages the GPU resources for inference. Option E is incorrect. Snowpark Container Services is specifically designed to ease the restrictions of warehouse deployment, allowing for the use of any packages (including PyPl) and enabling large models to run on distributed clusters of GPUs, which is ideal for this scenario.