Your AI team is deploying a large-scale inference service that must process real-time data 24/7. Given the high availability requirements and the need to minimize energy consumption, which approach would best balance these objectives?
Correct Answer: A
Implementing an auto-scaling group of GPUs (A) adjusts the number of active GPUs dynamically based on workload demand, balancing high availability and energy efficiency. This approach, supported by NVIDIA GPU Operator in Kubernetes or cloud platforms like AWS/GCP with NVIDIA GPUs, ensures 24/7 real-time processing by scaling up during peak loads and scalingdown during low demand, reducing idle power consumption. NVIDIA's power management features further optimize energy use per active GPU. * Fixed GPU cluster at 50% capacity(B) wastes resources during low demand and may fail during peaks, compromising availability. * Batch processing off-peak(C) sacrifices real-time capability, unfit for 24/7 requirements. * Single GPU at full capacity(D) risks overload, lacks redundancy, and consumes maximum power continuously. Auto-scaling aligns with NVIDIA's recommended practices for efficient, high-availability inference (A).
Question 47
In a data center, what is the purpose and benefit of a DPU?
Correct Answer: D
A Data Processing Unit (DPU) is a programmable processor that offloads, accelerates, and isolates infrastructure workloads-like networking, storage, and security-from the CPU. This enhances performance, reduces CPU overhead, and improves security by segregating tasks, benefiting AI data centers. It doesn't handle backups or physical infrastructure directly, focusing instead on compute efficiency. (Reference: NVIDIA DPU Documentation, Overview Section)
Question 48
In an AI cluster, what is the importance of using Slurm?
Correct Answer: D
Slurm (Simple Linux Utility for Resource Management) is a workload manager critical for AI clusters, handling job scheduling and resource allocation. It ensures tasks are assigned to available GPUs/CPUs efficiently, supporting scalable training and inference. It doesn't manage storage, perform training, or interconnect nodes--those are separate functions.
Question 49
Which two components are included in GPU Operator? (Choose two.)
Correct Answer: A,C
The NVIDIA GPU Operator is a tool for automating GPU resource management in Kubernetes environments. It includes two key components: GPU drivers, which provide the necessary software to interface with NVIDIA GPUs, and the NVIDIA Data Center GPU Manager (DCGM), which offers health monitoring, telemetry, and diagnostics for GPU clusters. Frameworks like PyTorch and TensorFlow are separate AI development tools, not part of the GPU Operator, which focuses on infrastructure rather than application layers. (Reference: NVIDIA GPU Operator Documentation, Components Section)
Question 50
Which are three key features of InfiniBand networking technology?
Correct Answer: D
InfiniBand is renowned for three key features: low latency (microsecond-scale communication), high bandwidth (100 Gb/s and beyond), and CPU offloads (via RDMA), which shift data transfer tasks to the network hardware, boosting system efficiency. High latency contradicts InfiniBand's design, and GPU offloads are not a core networking feature, making low latency, high bandwidth, and CPU offloads the definitive trio.
