You are designing a data center platform for a large-scale AI deployment that must handle unpredictable spikes in demand for both training and inference workloads. The goal is to ensure that the platform can scale efficiently without significant downtime or performance degradation. Which strategy would best achieve this goal?
Your AI cluster is managed using Kubernetes with NVIDIA GPUs. Due to a sudden influx of jobs, your cluster experiences resource overcommitment, where more jobs are scheduled than the available GPU resources can handle. Which strategy would most effectively manage this situation to maintain cluster stability?
Your AI model training process suddenly slows down, and upon inspection, you notice that some of the GPUs in your multi-GPU setup are operating at full capacity while others are barely being used. What is the most likely cause of this imbalance?
Which is the best PUE value for a data center?
Your AI team is using Kubernetes to orchestrate a cluster of NVIDIA GPUs for deep learning training jobs.
Occasionally, some high-priority jobs experience delays because lower-priority jobs are consuming GPU resources. Which of the following actions would most effectively ensure that high-priority jobs are allocated GPU resources first?
