NCP-AII exam dumps

NCP-AII practice question 60 of 146

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

NCP-AII Question 60

Select 2

An organization is deploying an AI workload on an NVIDIA DGX system. The team is deciding between using a bare-metal setup and a virtualized environment to maximize performance and scalability. Which of the following factors should they prioritize when choosing their system setup?

  1. A

    Low-latency communication between GPUs for model training

  2. B

    Simplified resource sharing and isolation for multi-team collaboration

  3. C

    Direct access to NVIDIA CUDA libraries for optimized performance

  4. D

    Ease of migrating workloads between different servers

  5. E

    Availability of pre-configured AI frameworks to accelerate deployment

Show answer and explanation

Correct answers: A, C

Explanation

For high-performance AI workloads on NVIDIA systems, factors like low-latency GPU communication and direct access to NVIDIA CUDA libraries are critical. These factors are best supported by bare-metal setups, which eliminate the overhead associated with virtualization. While other considerations like resource sharing, workload migration, and pre-configured frameworks are useful, they do not directly address the performance and scalability needs of demanding AI workloads.

  • A. Correct.

    Low-latency GPU communication is essential for high-performance AI workloads, especially during model training, and is best achieved in a bare-metal setup.

  • B. Incorrect.

    While resource sharing and isolation are useful in virtualized environments, they can introduce overhead and are not critical for maximizing performance in high-demand AI workloads.

  • C. Correct.

    Direct access to NVIDIA CUDA libraries ensures optimal performance, which is crucial for AI workloads and is typically better supported in bare-metal environments.

  • D. Incorrect.

    Ease of migrating workloads is more relevant to virtualized environments, but it does not directly impact performance optimization in AI workloads.

  • E. Incorrect.

    Pre-configured AI frameworks are helpful for deployment but do not influence the choice between bare-metal and virtualized environments directly.

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