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NVIDIA AI Infrastructure Sample Questions:

1. You have installed the NVIDIA Container Toolkit and are attempting to run a container with GPU support. However, the 'docker run' command fails with an error indicating that the NVIDIA runtime is not found. You have already verified that the NVIDIA Container Toolkit is installed, and the Docker daemon has been restarted. What is the most likely cause of this error?

A) The NVIDIA driver version is incompatible with the CUDA version specified in the container image.
B) The '/etc/docker/daemon.json' file is missing or has incorrect configuration settings related to the NVIDIA runtime.
C) The 'nvidia-container-runtime' package is not installed.
D) The container image is corrupted and needs to be rebuilt.
E) The system doesn't have a GPU.

2. You are running a distributed training job on a multi-GPU server. After several hours, the job fails with a NCCL (NVIDIA Collective Communications Library) error. The error message indicates a failure in inter-GPU communication. 'nvidia-smi' shows all GPUs are healthy. What is the MOST probable cause of this issue?

A) Insufficient inter-GPU bandwidth; reduce the batch size to decrease communication overhead.
B) A bug in the NCCL library itself; downgrade to a previous version of NCCL.
C) A faulty network cable connecting the server to the rest of the cluster.
D) Incorrect NCCL configuration, such as an invalid network interface or incorrect device affinity settings.
E) Driver incompatibility issue between NCCL and the installed NVIDIA driver version.

3. You are tasked with selecting transceivers for a new NVIDIA Quantum-2 InfiniBand switch deployment. The primary requirement is to minimize power consumption while maintaining 400Gbps bandwidth over short distances (up to 50 meters). Which transceiver type would offer the BEST power efficiency in this scenario?

A) QSFP-DD LR8
B) QSFP-DD DR4
C) QSFP-DD SR4
D) QSFP-DD AOC
E) QSFP-DD SR8

4. Consider a scenario where you're using GPUDirect Storage to enable direct memory access between GPUs and NVMe drives. You observe that while GPUDirect Storage is enabled, you're not seeing the expected performance gains. What are potential reasons and configurations you should check to ensure optimal GPUDirect Storage performance? Select all that apply.

A) Ensure that the NVMe drives are connected to the system via PCle Gen4 or Gen5.
B) Disable CPU-side caching to force all I/O operations to go directly to the GPU memory.
C) Check if the file system supports direct I/O (e.g., using 'directio' mount option).
D) Confirm that the CUDA driver version is compatible with GPIJDirect Storage.
E) Verify that the NVMe drives are properly configured in a RAID 0 configuration.

5. You encounter a situation where a container running with GPU support is experiencing significant performance degradation compared to running the same application directly on the host. You have already verified that the NVIDIA drivers are correctly installed and the NVIDIA Container Toolkit is properly configured. Which of the following could be contributing factors to this performance difference?
(Select all that apply)

A) CPU pinning or NIJMA affinity is not properly configured for the container, leading to inefficient memory access.
B) The kernel version within the container is significantly different from the host kernel, leading to driver compatibility issues.
C) The container is using a significantly older version of the CUDA runtime compared to the host.
D) The '-ipc=host' flag is not used when running the container, causing inter-process communication overhead.
E) Insufficient bandwidth between CPU and GPU

Solutions: