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NVIDIA AI Infrastructure Sample Questions (Q110-Q115):

NEW QUESTION # 110
A GPU in your AI server consistently overheats during inference workloads. You've ruled out inadequate cooling and software bugs.
Running 'nvidia-smi' shows high power draw even when idle. Which of the following hardware issues are the most likely causes?

A. Insufficient system RAM.
B. Incorrectly seated GPU in the PCle slot, leading to poor power delivery.
C. Degraded thermal paste between the GPU die and the heatsink.
D. A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage.
E. A BIOS setting that is overvolting the GPU.

Answer: B,C,D

Explanation:
Degraded thermal paste loses its ability to conduct heat effectively. A failing VRM can cause excessive power draw and heat generation. An incorrectly seated GPU can cause instability and poor power delivery, leading to overheating. Overvolting in BIOS will definitely cause overheating. While insufficient RAM can cause performance issues, it is less likely to lead to overheating.

NEW QUESTION # 111
Consider a scenario where you need to run two different deep learning models, Model A and Model B, within separate Docker containers on the same NVIDIA GPU. Model A requires CUDA 11.2, while Model B requires CUDA 11.6. How can you achieve this while minimizing conflicts and ensuring each model has access to its required CUDA version?

A. Install both CUDA 11.2 and CUDA 11.6 on the host system and use 'CUDA VISIBLE DEVICES* to isolate each model to a specific CUDA version.
B. Use separate Docker images for each model, each based on the appropriate 'nvidia/cuda' image (e.g., 'nvidia/cuda:ll .2-base-ubuntu20.04' and nvidia/cuda: 1 1.6-base-ubuntu20.04 s).
C. Install both CUDA 11.2 and CUDA 11.6 inside each Docker container and use 'LD LIBRARY PATH' to switch between the CUDA versions for each model.
D. Create a single Docker image with both CUDA versions and dynamically link the correct CUDA libraries at runtime using environment variables.
E. Mount the CUDA libraries from the host machine into both containers using Docker volumes, ensuring each container has access to both CUDA versions.

Answer: B

Explanation:
The recommended and most straightforward approach is to use separate Docker images (B), each based on the specific nvidia/cuda' image version needed. This creates isolated environments, avoiding conflicts and ensuring each model has the correct CUDA toolkit. Installing multiple CUDA versions on the host (A) can lead to conflicts and isn't necessary with Docker. Installing multiple CUDA versions within a single container (C, D) adds complexity and potential conflicts. Mounting CUDA libraries from the host (E) might work, but it's less isolated and can create dependency management issues.

NEW QUESTION # 112
A BlueField-2 DPU is being used to accelerate storage access via NVMe-oF. You notice high CPU utilization on the host server, despite the DPU. What is the MOST likely cause and the MOST effective initial troubleshooting step?

A. The host is still handling a significant portion of the 1/0 processing due to incorrect offload settings; Check the NVMe-oF initiator settings and verify that offload is enabled.
B. Firmware mismatch between the host NIC driver and the BlueField DPU firmware; Upgrade both to the latest compatible versions.
C. Incorrect NVMe-oF target configuration on the storage array; Check target NQN and discovery settings.
D. Incorrect RoCE configuration; Verify RoCE version and MTU settings using 'roce_util'.
E. Insufficient memory allocated to the BlueField DPIJ; Increase memory allocation via the BMC.

Answer: A

Explanation:
The MOST likely cause of high host CPU utilization with a DPIJ accelerating NVMe-oF is that the host is still handling significant I/O processing. The initial step is to verify NVMe-oF initiator offload settings to ensure the DPIJ is actually handling the workload. Checking target configuration or RoCE, while important, wouldn't directly address high host CPU if the offload isn't working in the first place.

NEW QUESTION # 113
You are setting up a multi-node A1 cluster with NVIDIA GPUs and InfiniBand for inter-node communication. You need to ensure the InfiniBand network is functioning optimally for GPU-accelerated workloads. What steps would you take to validate the InfiniBand installation and performance?

A. Run 'ibstat' to check InfiniBand interface status, use 'ibping' and 'ibperf to test latency and bandwidth, and verify correct NCCL configuration (e.g., during a distributed training run.
B. Configure a static IP address on the InfiniBand interfaces, and rely on the operating system's network diagnostics.
C. Run 'ibstat' to check InfiniBand interface status, use 'ping' to test connectivity, and rely on NCCL's internal checks during training.
D. Use 'nvidia-smi' to monitor InfiniBand traffic, and rely on CUDA-aware MPl for communication validation.
E. Verify the InfiniBand drivers are installed and then run a standard TCP benchmark between the nodes.

Answer: A

Explanation:
Sibstat' verifies interface status. 'ibping' and 'ibperf are InfiniBand-specific tools for latency and bandwidth testing. NCCL (NVIDIA Collective Communications Library) is critical for distributed training, and provides valuable diagnostic information. The other options are either incomplete or rely on tools not specific to InfiniBand.

NEW QUESTION # 114
You are setting up a new AI inference server in a colocation facility. The server will be connected to a 100GbE switch managed by the facility. You have the option to use either a single-mode fiber connection with an LR4 transceiver or a multi-mode fiber connection with an SR4 transceiver. The distance between your server and the switch is approximately 75 meters. Considering cost, signal quality, and future scalability, which option is MOST suitable?

A. AOC cable, as it provides better future scalabilty than any other type of connection
B. LR4 transceiver with single-mode fiber, as it provides better power efficiency.
C. Either option is equally suitable; the choice is arbitrary.
D. SR4 transceiver with multi-mode fiber, as it is typically more cost-effective for shorter distances.
E. LR4 transceiver with single-mode fiber, as it provides better signal quality over distance.

Answer: D

Explanation:
For a distance of 75 meters, an SR4 transceiver with multi-mode fiber is the most suitable option. It is typically more cost-effective than LR4 transceivers and provides adequate signal quality for this distance. LR4 transceivers are designed for longer distances and are more expensive. While AOCs are convenient, SR4 with multimode fiber is more cost effective. Cost-effectiveness is the defining factor between them.

NEW QUESTION # 115
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Free PDF Quiz 2025 NVIDIA NCP-AII: Pass-Sure NVIDIA AI Infrastructure Exam Preparation

Valid NCP-AII Test Notes - Real NCP-AII Dumps Free

NVIDIA AI Infrastructure Sample Questions (Q110-Q115):

Prepare well and Pass the NVIDIA NCP-AII Exam on the first attempt

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