NCP-AII exam dumps

NCP-AII practice question 82 of 146

NVIDIA-Certified Professional AI Infrastructure. Professional level, NVIDIA. Free question with the correct answer and a full explanation.

NCP-AII Question 82

Select 3

An organization is preparing to deploy an AI workload on an NVIDIA GPU-enabled server and wants to validate that the hardware is functioning correctly. Which of the following steps should be performed to ensure the hardware can handle the intended workload effectively?

  1. A

    Run NVIDIA System Management Interface (nvidia-smi) to verify GPU visibility and utilization.

  2. B

    Perform a stress test using an AI workload or synthetic benchmark like NVIDIA TensorRT Inference Server.

  3. C

    Check the server's physical network connections to ensure there is no packet loss.

  4. D

    Verify the GPU driver and CUDA toolkit versions are compatible with the workload's requirements.

  5. E

    Run the operating system's default system diagnostics for CPU and memory to validate hardware.

Show answer and explanation

Correct answers: A, B, D

Explanation

To validate hardware operation for AI workloads, it is essential to ensure the GPUs are detected and operational (nvidia-smi), capable of handling the workload (stress testing or benchmarking), and configured with compatible drivers and toolkits. These steps ensure the hardware is ready and optimized for the intended AI workload.

  • A. Correct.

    Running nvidia-smi is a critical step to verify that the GPUs are properly detected and operational, as well as to check their current utilization and health metrics.

  • B. Correct.

    Performing a stress test or running a synthetic benchmark ensures that the GPUs can handle the expected workload under load conditions and are functioning as intended.

  • C. Incorrect.

    Checking network connectivity is important for distributed training setups but is not directly related to validating GPU hardware operation for workloads.

  • D. Correct.

    Verifying that the GPU driver and CUDA toolkit versions match the workload's requirements is essential, as incompatible versions can cause errors or degraded performance.

  • E. Incorrect.

    Running the operating system's default CPU and memory diagnostics is unrelated to validating GPU hardware. Specialized tools like nvidia-smi are required for GPU-specific validation.

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