Web-Based NVIDIA NCP-AII Practice Exam - Compatible with all OS

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NVIDIA AI Infrastructure Sample Questions (Q10-Q15):

NEW QUESTION # 10
You're profiling the performance of a PyTorch model running on an AMD server with multiple NVIDIA GPUs. You notice significant overhead in the data loading pipeline. Which of the following strategies can help optimize data loading and improve GPU utilization?
Select all that apply.

Answer: A,C,E

Explanation:
Using multiple worker processes in 'DataLoader' enables parallel data loading. Asynchronous prefetching allows data to be loaded while the GPU is processing the current batch. A faster storage system reduces the 1/0 bottleneck. Loading the entire dataset into RAM might not be feasible for large datasets. Reducing batch size reduces the amount of data loaded but could decrease overall GPU utilizatiom


NEW QUESTION # 11
Which of the following statements regarding the benefits of using a BlueField DPU for network offload are TRUE? (Select TWO)

Answer: B,E

Explanation:
BlueField DPUs offload network tasks, freeing up CPU resources and improving network performance through hardware acceleration. Simplified configuration is not always guaranteed, as DPUs often require more configuration due to their advanced features. DPUs do not eliminate the need for a NIC, they are a NIC with advanced processing capabilities.


NEW QUESTION # 12
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?

Answer: A

Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.


NEW QUESTION # 13
ClusterKit's NCCL bandwidth test shows 350 GB/s on a 400G InfiniBand fabric. How should this result be interpreted?

Answer: C

Explanation:
Interpreting NCCL (NVIDIA Collective Communications Library) results requires understanding the difference between theoretical link rate and effective bus bandwidth. A 400G (NDR) InfiniBand link has a theoretical unidirectional capacity of 50 GB/s. In an 8-GPU DGX system, where each GPU is typically mapped to its own 400G HCA, the aggregate theoretical bandwidth is 400 GB/s. However, NCCL performance benchmarks reportBus Bandwidth, which accounts for the mathematical overhead of collective operations like all_reduce. Achieving350 GB/s(Option A) is widely considered optimal and representative of a healthy, well-configured fabric. This indicates that the system is effectively utilizing GPUDirect RDMA to bypass host memory and that the network switches are routing traffic without significant congestion or packet drops. Expecting >390 GB/s (Option C) is unrealistic once protocol headers and algorithmic overhead are subtracted from the raw bit rate. Rerunning with CPU stress (Option D) would not provide insights into the InfiniBand/GPU fabric health.


NEW QUESTION # 14
After ClusterKit reports " GPU-Host latency exceeds threshold, " which NVIDIA diagnostic tool should be used to isolate hardware faults?

Answer: B

Explanation:
" GPU-Host latency " issues in NVIDIA DGX or HGX systems are frequently caused by incorrect PCIe affinity or sub-optimal NUMA (Non-Uniform Memory Access) mapping. If a GPU is forced to communicate with a CPU core or an HCA that is not on its local PCIe switch/root complex, latency increases significantly as data must cross the QPI/UPI inter-processor links. The command nvidia-smi topo -m provides a detailed matrix of the system ' s internal topology, showing how GPUs, CPUs, and NICs are connected. It identifies whether the connection is via a single PCIe switch (PIX), multiple switches (PXB), or across the CPU (SYS).
By inspecting this map, an administrator can identify if a software process is pinned to the wrong NUMA node or if a hardware path is unexpectedly degraded. While DCGM (Option C) is good for checking component health, it doesn ' t map the logical-to-physical affinity paths that cause specific latency " threshold
" warnings.


NEW QUESTION # 15
......

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