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NVIDIA AI Infrastructure Sample Questions:

1. After physically installing a new NVIDIA GPU in a server, you boot the system. You notice that the GPU is not recognized by the operating system. You've verified the card is properly seated and powered. What are the MOST LIKELY causes and solutions? (Select TWO)

A) The PCIe slot is faulty. Solution: Try installing the GPU in a different PCIe slot.
B) The GPU is not compatible with the operating system. Solution: Reinstall the operating system.
C) The motherboard BIOS/UEFI does not support the GPU. Solution: Update the motherboard BIOS/UEFI to the latest version.
D) The incorrect GPU drivers are installed or no drivers are installed at all. Solution: Download and install the latest drivers from the NVIDIA website.
E) The GPU is defective. Solution: Return the GPU to the manufacturer.

2. A user reports that their GPU-accelerated application is crashing with a CUDA error related to 'out of memory'. You have confirmed that the GPU has sufficient physical memory What are the likely causes and troubleshooting steps?

A) The application is leaking GPU memory. Use a memory profiling tool like 'cuda-memcheck' to identify the source of the leak.
B) The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.
C) The system's virtual memory is exhausted. Increase the swap space.
D) The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.
E) The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.

3. You are setting up a multi-node A1 cluster with NVIDIA GPUs and InfiniBand for inter-node communication. You need to ensure the InfiniBand network is functioning optimally for GPU-accelerated workloads. What steps would you take to validate the InfiniBand installation and performance?

A) Run 'ibstat' to check InfiniBand interface status, use 'ibping' and 'ibperf to test latency and bandwidth, and verify correct NCCL configuration (e.g., during a distributed training run.
B) Verify the InfiniBand drivers are installed and then run a standard TCP benchmark between the nodes.
C) Use 'nvidia-smi' to monitor InfiniBand traffic, and rely on CUDA-aware MPl for communication validation.
D) Configure a static IP address on the InfiniBand interfaces, and rely on the operating system's network diagnostics.
E) Run 'ibstat' to check InfiniBand interface status, use 'ping' to test connectivity, and rely on NCCL's internal checks during training.

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

5. You suspect a power supply issue is causing intermittent GPU failures in a server with four NVIDIAAIOO GPUs. The server is rated for a peak power consumption of 3000W. You have a power meter available. Which of the following methods provides the most accurate assessment of the server's power consumption under full GPU load?

A) Use the power meter to measure the server's power consumption while running a synthetic benchmark that fully utilizes all GPIJs simultaneously.
B) Check the server's BIOS for power consumption readings.
C) Add the maximum power rating of each GPU to the CPU's TDP (Thermal Design Power).
D) Run 'nvidia-smi' and sum the reported power consumption for each GPIJ.
E) Use the power meter to measure the server's power consumption at idle and multiply by four.

Solutions: