Correct Option: C

Cost-Effective GPU Usage: Preemptible VMs are significantly cheaper, making them an ideal choice for long-running, non-restartable jobs that can tolerate interruptions.

GPU Utilization: Attaching GPUs to preemptible VMs ensures your machine learning pipelines have the necessary computational power for efficient image processing model training.

Flexible Scaling: While autoscaling adapts to workload fluctuations, preemptible VMs offer a more budget-friendly solution for consistent, long-running tasks.

Why other options are less suitable:

• Option A (Node Auto-Provisioning): Automatically adjusts node count based on demand but doesn't specifically cater to the need for GPU resources in long-running jobs.
• Option B (VerticalPodAutoscaler): Optimizes pod resource requests and limits but doesn't offer a cost-effective GPU solution.
• Option D (Autoscaling Node Pool with GPUs): Provides GPU resources but may not be as cost-efficient as preemptible VMs for non-restartable, long-running jobs.