A data center is running a cluster of NVIDIA GPUs to support various AI workloads. The operations team needs to monitor GPU performance to ensure workloads are running efficiently and to prevent effectively? (Select two)
potential hardware failures. Which two key measures should they focus on to monitor the GPUs
Correct Answer: C, D
Explanation:
To monitor GPU performance effectively in an AI data center, the focusshould be on metrics directly
tied to GPU health and efficiency: GPU temperature and power consumption(C) are critical to prevent overheating and power-related failures, which can disrupt workloads or damage hardware. High temperatures or excessive power draw indicate potential issues requiring intervention. GPU memory utilization(D) reflects how much of the GPU’s memory is being used by workloads. High utilization can lead to memory bottlenecks, while low utilization might indicate underuse, both affecting efficiency. Disk I/O rates(A) relate to storage performance, not GPU operation directly. CPU clock speed(B) is a CPU metric, irrelevant to GPU monitoring in this context. Network bandwidth usage(E) is important for distributed systems but doesn’t directly assess GPU performance or health. NVIDIA tools like NVIDIA System Management Interface (nvidia-smi) provide these metrics (C and D), making them essential for monitoring. Reference:NVIDIA Data Center GPU Management documentation; nvidia-smi usage guide on nvidia.com.
Question 2
A large enterprise is deploying a high-performance AI infrastructure to accelerate its machine learning workflows. They are using multiple NVIDIA GPUs in a distributed environment. To optimize the workload distribution and maximize GPU utilization, which of the following tools or frameworks should be integrated into their system? (Select two)
Correct Answer: A, D
Explanation:
In a distributed environment with multiple NVIDIA GPUs, optimizing workload distribution and GPU
utilization requires tools that enable efficient computation and communication: NVIDIA CUDA(A) is a foundational parallel computing platform that allows developers to harness GPU power for general-purpose computing, including machine learning. It’s essential for programming GPUs and optimizing workloads in a distributed setup. NVIDIA NCCL(D) (NVIDIA Collective Communications Library) is designed for multi-GPU and multinode communication, providing optimized primitives (e.g., all-reduce, broadcast) for collective operations in deep learning. It ensures efficient data exchange between GPUs, maximizing utilization in distributed training. NVIDIA NGC(B) is a hub for GPU-optimized containers and models, useful for deployment but not directly responsible for workload distribution or GPU utilization optimization. TensorFlow Serving(C) is a framework for deploying machine learning models for inference, not for optimizing distributed training or GPU utilization during model development. Keras(E) is a high-level API for building neural networks, but it lacks the low-level control needed for distributed workload optimization, it relies on backends like TensorFlow or CUDA. Thus, CUDA (A) and NCCL (D) are the best choices for this scenario. Reference: NVIDIA CUDA Toolkit documentation; NVIDIA NCCL documentation on nvidia.com
Question 3
In an AI cluster, what is the purpose of job scheduling?
Correct Answer: C
Explanation:
Job scheduling in an AI cluster assigns workloads (e.g., training, inference) to available compute resources (GPUs, CPUs), optimizing resource utilization and ensuring efficient execution. It’s distinct from data analysis, monitoring, or software management, focusing solely on workload distribution.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Job Scheduling)
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