NVIDIA AI Infrastructure : NCP-AII

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NVIDIA AI Infrastructure Sample Questions:

1. You are troubleshooting a network performance issue in your NCP-AII environment. After running 'ibstat' on a host, you see the following output for one of the InfiniBand ports:

What does the 'LMC: 0' indicate, and what are the implications for network performance?

A) LMC : 0 is the default and expected value; it has no impact on performance.
B) LMC : 0 indicates the port is operating at the lowest possible speed.
C) LMC : 0 indicates that the Subnet Manager is not running correctly.
D) LMC: 0 indicates that the link is down and not functioning correctly.
E) LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.

2. You are attempting to install NGC CLI on a CentOS 7 system, but the 'pip install nvidia-cli' command fails with a 'Could not find a version that satisfies the requirement nvidia-cli' error. You have confirmed that 'pip' is installed and working. What could be the cause of this issue?

A) The system's firewall is blocking access to the Python Package Index (PyPl). CentOS 7 is not supported by NGC CLI.
B) The CentOS 7 system does not have the required Python version installed. NGC CLI requires Python 3.6 or later.
C) The 'pip' version is outdated and incompatible with the NGC CLI package. Upgrade 'pip' using 'pip install -upgrade pip'.
D) The system's package manager (YUM) is not configured correctly, preventing 'pip' from finding the NGC CLI package.

3. You are tasked with creating a custom Docker image for a deep learning application that requires a specific version of cuDNN. You want to minimize the image size while ensuring that the cuDNN libraries are correctly installed and configured. What is the most efficient way to achieve this?

A) Use a multi-stage Docker build, using a base image with the desired CUDA version for building and then copying only the necessary cuDNN libraries to a smaller runtime image.
B) Download the cuDNN archive from NVIDIA, extract the libraries, and manually copy them to the appropriate locations within the Dockerfile.
C) Use the NVIDIA Container Toolkit to dynamically inject the cuDNN libraries into the container at runtime.
D) Install the entire CUDA toolkit within the Docker image, even if only cuDNN is needed.
E) Use a pre-built CUDA base image and install cuDNN during the container run.

4. When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?

A) Minimizing the number of hops between GPUs that need to communicate frequently.
B) Maximizing the distance between servers to improve cooling.
C) Placing servers near the network's edge to reduce latency.
D) Using a star topology for simplified management.
E) Ensuring all servers are on the same subnet for ease of configuration.

5. You are deploying an NVIDIA GPU-accelerated application in a virtualized environment using vGPU. How does vGPU technology impact power and cooling considerations compared to a bare-metal deployment, and what specific monitoring metrics become crucial?

A) vGPU deployments typically require less power and cooling than bare-metal, as resources are shared. The host server's overall power consumption becomes the primary monitoring metric.
B) vGPU deployments require specialized cooling solutions that are not needed for bare metal setups.
C) vGPU deployments can lead to higher overall power consumption and concentrated heat generation on the host server due to resource consolidation. Monitoring metrics like GPU utilization per VM, vGPU frame rate, and host server thermal headroom become crucial.
D) vGPU deployments eliminate the need for GPU monitoring, as the virtualization layer handles all power and cooling management.
E) vGPU deployments have no significant impact on power and cooling requirements compared to bare-metal. Standard GPU temperature and power draw metrics are sufficient.

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