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NVIDIA AI Infrastructure Sample Questions:

1. Which of the following is the MOST important reason for using a dedicated storage network (e.g., InfiniBand or RoCE) for AI/ML workloads compared to using the existing Ethernet network?

A) Lower latency and higher bandwidth for data transfer.
B) Simplified network management and configuration.
C) Improved security due to network isolation.
D) Automatic Quality of Service (QOS) prioritization for AI/ML traffic.
E) Reduced cost compared to upgrading the existing Ethernet infrastructure.

2. Consider a scenario where you are setting up a high-performance computing cluster with several GPU-accelerated nodes using Slurm as the resource manager. You want to ensure that jobs requesting GPUs are only scheduled on nodes with the appropriate NVIDIA drivers and CUDA toolkit installed. How can you achieve this within Slurm?

A) Use Slurm's 'GresTypeS configuration option in 'slurm.conf to define a generic resource type called 'gpu' and then configure each node to advertise the available GPIJs. Slurm will automatically ensure that jobs requesting GPUs are only scheduled on nodes with the 'gpu' resource.
B) Create a custom Slurm script that checks for the presence of the NVIDIA driver and CUDA toolkit before submitting a job to a node. If the requirements are not met, the job is rejected.
C) Utilize Slurm's Prolog and Epilog scripts to dynamically install the necessary NVIDIA drivers and CUDA toolkit on each node before and after a job runs. This ensures that the required software is always available.
D) Install the NVIDIA Data Center GPU Manager (DCGM) on each node and configure Slurm to query DCGM for GPU availability and health. Slurm will then only schedule jobs on healthy and available GPUs.
E) Use Slurm's node features to tag nodes with the "Feature=' keyword in 'slurm.conf. For example, tag nodes with GPUs as 'Feature=gpu' . Jobs can then request nodes with the 'gpu' feature using the option.

3. You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You've already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue?

A) Re-seat the GPU in its PCle slot to ensure proper contact and heat dissipation.
B) Reduce the ambient temperature of the data center.
C) Lower the GPU power limit using 'nvidia-smi -power-limit*.
D) Update the NVIDIA drivers to the latest version.
E) Increase the fan speed of the GPU cooler using 'nvidia-smi --fan'.

4. You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?

A) Downgrade the application to use CUDA 11.8 to match the host's driver version.
B) Install CUDA 12 inside the Docker container and set the 'CUDA DRIVER VERSION' environment variable to match the host driver version.
C) Mount the CUDA 12 libraries from a separate Docker volume into the container and configure the accordingly.
D) Use a Docker image based on 'nvidia/cuda:12.0-base-ubuntu20.04'. The NVIDIA Container Toolkit will automatically handle the driver compatibility between the host and the container.
E) Upgrade the NVIDIA driver on the host system to the latest version compatible with CUDA 12.

5. You are configuring a BlueField DPU to run a custom packet processing application. You want to ensure that the application has exclusive access to certain CPU cores on the DPU. Which mechanism is best suited for isolating CPU cores for your application on the Bluefield DPU?

A) Adjusting the kernel's scheduler parameters to prioritize the application's threads on the desired cores.
B) Modifying the DPU's bootloader configuration to disable the cores you want to reserve.
C) Using 'taskset' command to pin the application's processes to specific cores.
D) Using CPIJ affinity settings within the application code itself.
E) Utilizing cgroups (control groups) to create a dedicated cgroup for the application and limit its CPU usage to specific cores.

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