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Your organization operates an AI cluster where various deep learning tasks are executed. Some tasks are time- sensitive and must be completed as soon as possible, while others are less critical. Additionally, some jobs can be parallelized across multiple GPUs, while others cannot. You need to implement a job scheduling policy that balances these needs effectively. Which scheduling policy would best balance the needs of time-sensitive tasks and efficiently utilize the available GPUs?
Correct Answer: D
A priority-based scheduling system considering GPU availability and task parallelization best balances time- sensitive tasks and GPU utilization. It prioritizes urgent jobs while optimizing resource allocation (e.g., via Kubernetes with NVIDIA GPU Operator). Option A (FCFS) ignores priority. Option B (longest first) delays critical tasks. Option C (round-robin) neglects urgency and parallelization. NVIDIA's orchestration docs support priority-based scheduling.