PASS GUARANTEED QUIZ 2025 NCP-AII: NVIDIA AI INFRASTRUCTURE–RELIABLE EXAM DISCOUNT VOUCHER

Pass Guaranteed Quiz 2025 NCP-AII: NVIDIA AI Infrastructure–Reliable Exam Discount Voucher

Pass Guaranteed Quiz 2025 NCP-AII: NVIDIA AI Infrastructure–Reliable Exam Discount Voucher

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NVIDIA AI Infrastructure Sample Questions (Q99-Q104):

NEW QUESTION # 99
You've deployed a GPU-accelerated application in Kubernetes using the NVIDIA device plugin. However, your pods are failing to start with an error indicating that they cannot find the NVIDIA libraries. Which of the following could be potential causes of this issue? (Multiple Answers)

  • A. The 'nvidia-container-runtime' is not configured as the default runtime for Docker/containerd.
  • B. The application container image does not include the necessary NVIDIA libraries.
  • C. The NVIDIA device plugin is not properly configured in the Kubernetes cluster.
  • D. The NVIDIA drivers are not installed on the host node.
  • E. The GPU's compute capability is not sufficient for the workload.

Answer: A,B,C,D

Explanation:
If pods cannot find NVIDIA libraries, it could be because the drivers are missing on the host, the container runtime is not configured to use the NVIDIA runtime, the NVIDIA device plugin is misconfigured preventing GPU discovery and allocation, or the application container image does not include the NVIDIA libraries. E is likely incorrect, if the GPU's compute capability is insufficient then the app would likely start, but throw an error when trying to use the GPU.


NEW QUESTION # 100
A data scientist reports that training performance on a DGX A100 server has significantly degraded over the past week. 'nvidia-smi' shows all GPUs functioning, but 'nvprof' reveals substantially increased 'cudaMemcpy' times. What is the MOST likely bottleneck?

  • A. The storage system is slow, delaying data loading and preprocessing.
  • B. The PCle bus is saturated, limiting data transfer speeds between the CPU and GPUs.
  • C. The GPUs are overheating, causing thermal throttling and slower memory transfers.
  • D. The CPU is heavily loaded, causing contention for system memory bandwidth.
  • E. The NVLink connections between GPUs are failing, forcing data transfers through PCle.

Answer: D

Explanation:
Increased ScudaMemcpy' times indicate a bottleneck in data transfer between the CPU and GPIJs or within the GPU memory itself. While PCle saturation or failing NVLink connections could contribute, a heavily loaded CPU is often the primary culprit. CPU-bound preprocessing tasks or general system load can create contention for system memory, slowing down data movement to the GPUs. The NVLink being used is less likely because 'nvidia-smi' would likely report errors or lower speeds between the GPUs


NEW QUESTION # 101
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. Incorrectly seated GPU in the PCle slot, leading to poor power delivery.
  • B. A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage.
  • C. Insufficient system RAM.
  • D. A BIOS setting that is overvolting the GPU.
  • E. Degraded thermal paste between the GPU die and the heatsink.

Answer: A,B,E

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 # 102
A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?

  • A. The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
  • B. There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.
  • C. The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
  • D. A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.
  • E. A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.

Answer: B

Explanation:
Elevated inlet temperatures, despite normal GPU temperatures, strongly suggest an airflow issue. GPUs 3 and 4 are likely positioned in a way that restricts airflow. The first step is to check fan speeds and for any physical obstructions blocking airflow. Replacing components without addressing the airflow issue will not solve the problem.


NEW QUESTION # 103
Which of the following statements accurately describe the benefits of using MIG (Multi-lnstance GPU) in an AI/HPC environment?
(Select all that apply)

  • A. MIG guarantees performance isolation between different workloads running on the same GPIJ.
  • B. MIG eliminates the need for GPU virtualization software.
  • C. MIG allows running multiple CUDA versions simultaneously on the same physical GPIJ.
  • D. MIG increases the overall FLOPS of the GPU.
  • E. MIG allows a single GPU to be partitioned into multiple isolated instances, improving resource utilization.

Answer: A,E

Explanation:
MIG allows partitioning a single GPU into multiple isolated instances, thus optimizing resource utilization (A). It provides hardware- level isolation, ensuring performance consistency and preventing interference between workloads (B). While MIG simplifies GPU sharing, it doesn't eliminate the need for all virtualization software, especially in more complex environments. MIG doesn't increase FLOPS (D); it divides the existing compute power. While different containers running on different MIG instances could have different CUDA versions, MIG itself doesn't directly handle this (E); this is handled via containers or environments.


NEW QUESTION # 104
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