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NVIDIA AI Infrastructure Sample Questions:

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

2. You have a deep learning application that requires a specific version of the CUDA toolkit inside the container. How should you best ensure that the correct CUDA version is available within the container, considering the NVIDIA Container Toolkit is installed on the host?

A) Use a base image (e.g., from NVIDIA NGC) that already includes the desired CUDA toolkit version. This approach provides a consistent and reproducible environment.
B) Manually copy the necessary CUDA libraries from the host into the container using 'docker cp' before running the application.
C) Specify the desired CUDA version when running the container using the '-env flag. The NVIDIA Container Toolkit will dynamically install the CUDA version during container startup.
D) Use the nvidia-container-cli to modify the existing image to install the proper cuda version.
E) Install the required CUDA toolkit version directly on the host operating system. The NVIDIA Container Toolkit will automatically map it into the container.

3. You have a server with 8 NVIDIA A100 GPUs. You want to configure each GPU to be used by a different user, ensuring resource isolation and preventing one user's workload from monopolizing the entire GPU. Which NVIDIA technology is most suitable for this scenario?

A) NVIDIA NVLink
B) NVIDIA CUDA MPS (Multi-Process service)
C) NVIDIA vGPU (virtual GPU)
D) NVIDIA MIG (Mufti-instance GPU)
E) NVIDIA SLI (Scalable Link Interface)

4. A large language model (LLM) training job is running across multiple NVIDIAAI 00 GPUs in a cluster. You observe that the GPUs within a single server are communicating efficiently via NVLink, but the inter-server communication over Ethernet is becoming a bottleneck. Which of the following strategies, focusing on cable and transceiver selection, would MOST effectively address this inter-server communication bottleneck? (Choose TWO)

A) Replace existing Cat6 Ethernet cables with Cat8 cables.
B) Reduce the batch size of the LLM training job.
C) Implement InfiniBand as the interconnect technology for inter-server communication, utilizing appropriate InfiniBand cables and transceivers.
D) Upgrade inter-server connections to the highest available Ethernet speed (e.g., from 100GbE to 400GbE) using appropriate transceivers and fiber optic cables.
E) Replace all existing transceivers with Active Optical Cables (AOCs).

5. You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?

A) Enable CUDA graph capture to reduce kernel launch overhead.
B) Ensure that all matrix multiplications are performed using FP16 precision.
C) Pad the input tensors to dimensions that are multiples of 8 for optimal Tensor Core alignment.
D) Increase the batch size to fully utilize the available GPU memory.
E) Decrease the learning rate to improve training stability and reduce the need for gradient clipping.

Solutions: