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NVIDIA AI Infrastructure Sample Questions:

1. Which of the following commands or tools can be used to verify the NVIDIA driver version and the CUDA version installed on a Linux system?

A) 'nvidia-smr
B) 'cat /proc/driver/nvidia/version'
C) "Ispci I grep NVIDIA'
D) nvcc -version'
E) 'modinfo nvidia'

2. You've replaced a faulty NVIDIA Quadro RTX 8000 GPU with an identical model in a workstation. The system boots, and 'nvidia-smi' recognizes the new GPU. However, when rendering complex 3D scenes in Maya, you observe significantly lower performance compared to before the replacement. Profiling with the NVIDIA Nsight Graphics debugger shows that the GPU is only utilizing a small fraction of its available memory bandwidth. What are the TWO most likely contributing factors?

A) The workstation's power plan is set to 'Power Saver,' limiting GPU performance.
B) The new GPU's PCle link speed is operating at a lower generation (e.g., Gen3 instead of Gen4).
C) The Maya scene file contains corrupted or inefficient geometry.
D) The newly installed GPU's VBIOS has not been properly flashed, causing an incompatibility issue.
E) The NVIDIA OptiX denoiser is not properly configured or enabled.

3. After installing a new NVIDIA GPU, you attempt to run a CUDA application, but you encounter the following error: 'CUDA error: CUDA driver version is insufficient for CUDA runtime version'. You have verified the driver and CUDA toolkit are installed. What is the MOST likely reason for this error, and how do you resolve it?

A) The CUDA VISIBLE DEVICES environment variable is not set correctly.
B) The CUDA toolkit is too old. Update the CIJDA toolkit.
C) The GPU is not compatible with the CUDA toolkit. Install a different GPIJ.
D) The CUDA runtime libraries are missing from the system path. Add them to the PATH variable.
E) The NVIDIA driver is too old for the CUDA toolkit. Update the NVIDIA driver to a version that supports the CUDA toolkit.

4. Consider a distributed training job running across multiple nodes, each with local NVMe storage. You want to minimize network traffic and maximize I/O performance. Which data loading strategy would be MOST effective?

A) Centralized data loading from a single NFS server
B) Using rsync to copy data between nodes before each epoch
C) Using object storage (e.g., S3) as the primary data source and loading data on demand
D) Distributing the dataset across the local NVMe drives of each node and using a distributed data loader
E) Loading the entire dataset into the memory of a single node and then distributing it to the other nodes

5. Consider the following Dockerfile snippet:

This Dockerfile is used to build a deep learning application. After building and running a container from this image, you observe that the application is not detecting the GPU. You have verified that the NVIDIA Container Toolkit is installed and configured correctly on the host. What is the most likely reason for this issue?

A) The application code in 'app.py' is not explicitly requesting GPU resources.
B) The base image 'nvidia/cuda:ll .6.2-base-ubuntu20.04' does not include the necessary NVIDIA Container Toolkit components.
C) The 'requirements.txt' file is missing the 'nvidia-pyindex' package.
D) The 'docker run' command is missing the '-gpus all' flag.
E) The CUDA version on the host is different than the one specified in the Dockerfile.

Solutions: