LATEST NVIDIA NCP-AII EXAM DISCOUNT | PASS NCP-AII TEST GUIDE

Latest NVIDIA NCP-AII Exam Discount | Pass NCP-AII Test Guide

Latest NVIDIA NCP-AII Exam Discount | Pass NCP-AII Test Guide

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NVIDIA AI Infrastructure Sample Questions (Q47-Q52):

NEW QUESTION # 47
You are configuring an NVIDIAAIOO GPU in a server, and after installation and driver setup, lower than the GPU's specified TDP. What are the possible reasons for this? nvidia-smi reports a power limit much

  • A. The GPIJ is in a low-power mode due to inactivity.
  • B. The driver is not correctly installed.
  • C. The GPU is faulty.
  • D. The power supply is not providing enough power.
  • E. The system BIOS is limiting the power to the PCIe slot.

Answer: E

Explanation:
While the other options are possible, a BIOS setting restricting power to the PCIe slot is a common cause of unexpectedly low power limits reported by 'nvidia-smi'. Always check BIOS settings when troubleshooting power-related issues. The GPU should ramp up power if a workload is presented, if its in low power mode.


NEW QUESTION # 48
You're deploying BlueField OS to an Arm-based SmartNIC. After flashing the image, the system fails to boot and you observe a kernel panic related to device tree loading. Which of the following is the most likely cause?

  • A. The flashed image is not intended for your specific BlueField card revision. Ensure that image corresponds to hardware version.
  • B. Insufficient memory allocated to the initrd image. This can lead to failures during initial system setup.
  • C. The secure boot configuration is incorrectly set up. Disabling secure boot in the BIOS or bootloader might resolve the issue.
  • D. The BlueField OS image is corrupted. A fresh download and re-flash should resolve the problem.
  • E. Incorrect bootloader configuration (e.g., incorrect bootargs). The bootloader might not be pointing to the correct device tree blob (dtb) or root filesystem.

Answer: E

Explanation:
A device tree blob (DTB) describes the hardware to the kernel. If the bootloader doesn't point to the correct DTB or its boot arguments are incorrect, the kernel cannot properly initialize, leading to a panic. The other options could contribute to boot problems but a device tree problem commonly manifests as a kernel panic during initialization.


NEW QUESTION # 49
After installing a new NVIDIA GPU in an AI server, you run 'nvidia-smi' and receive the error 'NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.' You have verified the driver is installed. What is the MOST likely cause?

  • A. The system BIOS needs to be updated.
  • B. The power supply is insufficient to power the GPU.
  • C. The secure boot is enabled preventing the driver from initializing.
  • D. The GPU is not properly seated in the PCIe slot.
  • E. The wrong driver version is installed for the installed CUDA version.

Answer: D

Explanation:
A poorly seated GPU often leads to communication failures between the driver and the card. While power, BIOS, and driver versions can be issues, a seating problem is the most common first suspect. Secure boot issues are also possible.


NEW QUESTION # 50
Consider this scenario. You have a containerized A1 application that requires specific CUDA libraries. You want to manage the deployment and scaling of this application across multiple Kubernetes clusters, some of which might have different versions of NVIDIA drivers installed. How would you handle the CUDA dependency management in this multi-cluster environment to ensure compatibility and reproducibility?

  • A. Build a different container image for each Kubernetes cluster, specifically tailored to the driver version installed on that cluster.
  • B. Leverage an init container to install the correct CUDA libraries before the application container starts, based on the detected driver version.
  • C. Package the CUDA libraries directly into the application container image, ensuring that each cluster uses the same version.
  • D. Use the 'latest' tag when pulling your container image to ensure the most up-to-date version is used.
  • E. Use a multi-stage Docker build to dynamically download and install the appropriate CUDA libraries based on the driver version detected on each cluster.

Answer: C

Explanation:
Packaging the CUDA libraries into the container image ensures consistent behavior across all clusters, regardless of the driver version installed on each cluster's nodes. It is the most portable solution. Options B and C introduce runtime dependencies and increase complexity. Option D becomes unwieldy to manage. Option E is highly discouraged as the image can be updated without your knowledge.


NEW QUESTION # 51
You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?

  • A. Increase the batch size to fully utilize the available GPU memory.
  • B. Ensure that all matrix multiplications are performed using FP16 precision.
  • C. Decrease the learning rate to improve training stability and reduce the need for gradient clipping.
  • D. Pad the input tensors to dimensions that are multiples of 8 for optimal Tensor Core alignment.
  • E. Enable CUDA graph capture to reduce kernel launch overhead.

Answer: D

Explanation:
Padding input tensors (C) to multiples of 8 is crucial for optimal Tensor Core performance, as Tensor Cores operate most efficiently on data with these dimensions. Using FP16 (B) is important, but proper alignment is key for full utilization. Increasing batch size (A) can improve overall throughput but doesn't directly address Tensor Core utilization. CIJDA graph capture (D) reduces kernel launch overhead, not Tensor Core utilization directly. Decreasing learning rate (E) is unrelated to Tensor Core performance.


NEW QUESTION # 52
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