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NVIDIA AI Infrastructure Sample Questions:

1. During the physical installation of an NVIDIA GPU, you accidentally touch the gold connector pins on the card. What is the recommended course of action BEFORE inserting the GPU into the PCle slot?

A) Blow on the pins to remove any dust or debris.
B) Wipe the pins with a dry cloth.
C) Use compressed air to clean the pins.
D) Clean the pins with isopropyl alcohol and a lint-free swab, ensuring they are completely dry before installation.
E) It is okay to insert it directly as is.

2. After deploying BlueField OS, you notice that the network interfaces are not automatically configured with IP addresses. Which of the following actions would be the MOST appropriate first step to troubleshoot this issue?

A) Re-flash the Bluefield OS image
B) Manually assign static IP addresses to the interfaces using the 'ifconfig' command.
C) Reinstall the Mellanox OFED drivers. A corrupted driver installation could cause network configuration issues.
D) Check the DHCP client configuration to ensure it is enabled and properly configured to request IP addresses. Examine the logs for any errors.
E) Restart the networking service using 'systemctl restart networking'.

3. You have configured MIG on your A100 GPU, creating several MIG instances. You now want to allocate a specific MIG instance to a Docker container. How would you specify the necessary device option when running the 'docker run' command to ensure the container uses only that MIG instance? Assuming the MIG instance UUID is GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

A) docker run -nvidia-visible-devices GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
B) docker run -gpus all .
C) docker run -runtime=nvidia --device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...
D) docker run -gpus device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...
E) docker run -device=/dev/nvidia0 .

4. Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?

A) Using namespaces with resource quotas and network policies.
B) Using taints and tolerations to dedicate GPU nodes to specific workloads.
C) Using pod affinity and anti-affinity rules to control pod placement.
D) Using node affinity only.
E) Using labels and selectors to schedule workloads on specific GPU nodes.

5. 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.

Solutions: