Options A, B, and C are correct descriptions of SPCS service design models and their applications. Option A is correct: Jobs in SPCS are containerized tasks that execute and run to completion, making them ideal for finite operations like GPU-accelerated machine learning model training. Option B is correct: Services are designed for long-running applications, offering continuous availability and accessibility via internal and external endpoints, which is suitable for real-time inference in conversational interfaces. Option C is correct: Service Functions are callable computations that accept data as input, often from SQL queries. A key advantage is that data processing occurs within the Snowflake network boundary, making them efficient and secure for data-intensive tasks like batch inference. Option D is incorrect: While is a cost- effective CPU instance, GPU instances like 'GPU_NV_M' are explicitly optimized for 'intensive GPU usage scenarios like Computer Vision or LLMs/VLMs'. Therefore, using CPU-only instances for all LLM tasks, especially performance-critical ones, is not the general best practice. Option E is incorrect: Container images for Snowpark Container Services are stored in Snowflake's OCIv2 compliant Image Registry, not typically pulled directly from public Docker Hub repositories for deployment within Snowflake. The image registry has a unique hostname which allows OCI clients to access it via REST API calls, and images are pushed to image repositories within this registry.