100% PASS QUIZ 2025 NCP-AII: NVIDIA AI INFRASTRUCTURE–EFFICIENT NEW STUDY GUIDE

100% Pass Quiz 2025 NCP-AII: NVIDIA AI Infrastructure–Efficient New Study Guide

100% Pass Quiz 2025 NCP-AII: NVIDIA AI Infrastructure–Efficient New Study Guide

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Tags: NCP-AII New Study Guide, Answers NCP-AII Real Questions, NCP-AII Free Dump Download, Books NCP-AII PDF, NCP-AII Exam Preview

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NVIDIA AI Infrastructure Sample Questions (Q144-Q149):

NEW QUESTION # 144
You are designing a network for a distributed training job utilizing multiple GPUs across multiple nodes. Which network characteristic is MOST critical for minimizing training time?

  • A. Large MTU
  • B. High packet loss rate
  • C. Low latency
  • D. High bandwidth
  • E. Low cost

Answer: A,C,D

Explanation:
Distributed training involves frequent data exchange between GPUs. High bandwidth is necessary to move large datasets quickly. Low latency reduces the round-trip time for communication. Large MTIJ (Maximum Transmission Unit) can reduce overhead by transmitting larger packets. High packet loss is detrimental to performance. Low cost should not be prioritized over performance in a distributed training environment.


NEW QUESTION # 145
Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with "NCCL internal error, unhandled system error" after a recent CUDA update. The error occurs during the 'all reduce' operation.
What is the most likely root cause and how would you address it?

  • A. Incompatible NCCL version with the new CUDA version. Update NCCL to a version compatible with the installed CUDA version.
  • B. Firewall rules blocking inter-GPU communication. Configure the firewall to allow communication on the NCCL-defined ports (typically 8000-8010).
  • C. GPU Direct RDMA is not properly configured. Check 'dmesg' for errors and ensure RDMA is enabled.
  • D. Faulty network cables used for inter-node communication (if the training job spans multiple servers). Replace the network cables with certified high-speed cables.
  • E. Insufficient shared memory allocated to the CUDA context. Increase the shared memory limit using 'cudaDeviceSetLimit(cudaLimitSharedMemory, new_limity.

Answer: A

Explanation:
NCCL relies on specific CUDA versions. An incompatibility after a CUDA update is the most probable cause. Insufficient shared memory is less likely to cause a system error within NCCL. Firewall rules usually manifest as connection refused errors. Faulty network cables affect inter-node communication, not intra-node. While RDMA issues can cause problems, they typically don't present as 'unhandled system error' immediately after a CUDA update, and are more likely if RDMA was working previously.


NEW QUESTION # 146
You are upgrading an AI server with new NVIDIAA800 GPUs and require 400GbE connectivity. After installing the new QSFP-DD transceivers and connecting the fiber cables, the link does not come up. You suspect a polarity issue. Assuming you are using MPO/MTP connectors, which of the following steps would BEST help diagnose and rectify a potential polarity mismatch? (Choose TWO)

  • A. Replace the QSFP-DD transceivers with known working units.
  • B. Use a fiber optic polarity tester to confirm correct TX/RX mapping through the entire cable assembly.
  • C. Swap the transmit (TX) and receive (RX) fibers at one end of the connection.
  • D. Consult the cable manufacturer's documentation to verify the MPO/MTP key orientation and pinout configuration and ensure it aligns with the transceiver requirements.
  • E. Use an Optical Time Domain Reflectometer (OTDR) to verify cable integrity.

Answer: B,D

Explanation:
Polarity issues often arise with MPO/MTP connectors. Consulting the cable documentation to verify the key orientation is critical. A fiber optic polarity tester can definitively confirm the TX/RX mapping. Swapping fibers manually isn't recommended due to potential damage. OTDR checks cable integrity, but not polarity. Replacing transceivers is a troubleshooting step, but addressing polarity first is more efficient.


NEW QUESTION # 147
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?

  • A. Replace the existing PSU with a higher wattage PSU that has at least four 8-pin PCIe power connectors.
  • B. Connect the GPUs using the motherboard's internal SATA power connectors.
  • C. Underclock the GPUs significantly to reduce their power consumption below the available PSU capacity.
  • D. Install only three GPUs and leave the fourth unpowered.
  • E. Use a PCIe power splitter cable on one of the 8-pin connectors to power two GPUs.

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 # 148
You are running a distributed training job on a multi-GPU server. After several hours, the job fails with a NCCL (NVIDIA Collective Communications Library) error. The error message indicates a failure in inter-GPU communication. 'nvidia-smi' shows all GPUs are healthy. What is the MOST probable cause of this issue?

  • A. A bug in the NCCL library itself; downgrade to a previous version of NCCL.
  • B. Insufficient inter-GPU bandwidth; reduce the batch size to decrease communication overhead.
  • C. Driver incompatibility issue between NCCL and the installed NVIDIA driver version.
  • D. Incorrect NCCL configuration, such as an invalid network interface or incorrect device affinity settings.
  • E. A faulty network cable connecting the server to the rest of the cluster.

Answer: C,D

Explanation:
NCCL errors during inter-GPU communication often stem from configuration issues (B) or driver incompatibilities (E). Incorrect network interface or device affinity settings can prevent proper communication. Driver versions might not fully support the NCCL version being used. Reducing batch size (C) might alleviate symptoms but doesn't address the root cause. A faulty network cable (D) would likely cause broader network issues beyond NCCL. Downgrading NCCL (A) is a potential workaround but not the ideal first step.


NEW QUESTION # 149
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