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NVIDIA AI Infrastructure Sample Questions:

1. Which of the following techniques can be used to optimize storage performance for deep learning training?

A) Data sharding
B) Data deduplication
C) Data compression using lossless algorithms (e.g., gzip)
D) Using a larger block size for the file system
E) Data prefetching

2. You are upgrading an AI server with new NVIDIAA800 GPUs and require 400GbE connectivity. After installing the new QSFP-DD transceivers and connecting the fiber cables, the link does not come up. You suspect a polarity issue. Assuming you are using MPO/MTP connectors, which of the following steps would BEST help diagnose and rectify a potential polarity mismatch? (Choose TWO)

A) Consult the cable manufacturer's documentation to verify the MPO/MTP key orientation and pinout configuration and ensure it aligns with the transceiver requirements.
B) Replace the QSFP-DD transceivers with known working units.
C) Use a fiber optic polarity tester to confirm correct TX/RX mapping through the entire cable assembly.
D) Use an Optical Time Domain Reflectometer (OTDR) to verify cable integrity.
E) Swap the transmit (TX) and receive (RX) fibers at one end of the connection.

3. You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough. How can you reduce the load on the CPU and improve the overall training throughput?

A) Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.
B) Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GPU.
C) Move the training data to a local NVMe drive on the training node.
D) Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.
E) Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.

4. Consider a scenario where you need to run two different deep learning models, Model A and Model B, within separate Docker containers on the same NVIDIA GPU. Model A requires CUDA 11.2, while Model B requires CUDA 11.6. How can you achieve this while minimizing conflicts and ensuring each model has access to its required CUDA version?

A) Mount the CUDA libraries from the host machine into both containers using Docker volumes, ensuring each container has access to both CUDA versions.
B) Install both CUDA 11.2 and CUDA 11.6 inside each Docker container and use 'LD LIBRARY PATH' to switch between the CUDA versions for each model.
C) Use separate Docker images for each model, each based on the appropriate 'nvidia/cuda' image (e.g., 'nvidia/cuda:ll .2-base-ubuntu20.04' and nvidia/cuda: 1 1.6-base-ubuntu20.04 s).
D) Create a single Docker image with both CUDA versions and dynamically link the correct CUDA libraries at runtime using environment variables.
E) Install both CUDA 11.2 and CUDA 11.6 on the host system and use 'CUDA VISIBLE DEVICES* to isolate each model to a specific CUDA version.

5. An AI infrastructure uses a combination of air-cooled and liquid-cooled NVIDIA GPUs. You want to optimize cooling performance based on the specific thermal characteristics of each GPU type and their location within the server rack. How can you achieve granular cooling control and monitoring to address these heterogeneous cooling requirements effectively? SELECT TWO.

A) Implement rack-level airflow management solutions, such as blanking panels and cable management, to improve overall airflow uniformity.
B) Implement dynamic fan speed control based on individual GPU temperatures, leveraging tools like 'nvidia-smi' and custom scripts, for air-cooled GPUs.
C) Use a centralized monitoring system to track GPU temperatures and power consumption, but apply the same cooling profile to all GPUs regardless of type.
D) Employ liquid cooling only for the highest TDP GPUs and rely on ambient air cooling for all other components.
E) Deploy per-server cooling solutions with independent fan control for each server node, allowing for tailored airflow adjustments.

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