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NVIDIA AI Infrastructure Sample Questions:

1. A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?

A) There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.
B) The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
C) A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.
D) The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
E) A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.

2. A BlueField-3 DPUis configured to run both control plane and data plane functions. After a recent software update, you notice that the data plane performance has significantly degraded, but the control plane remains responsive. What is the MOST likely cause, assuming the update didn't introduce any code bugs, and what is the BEST approach to diagnose this issue?

A) Power throttling; Check the DPU's power consumption and thermal status via the BMC.
B) Driver incompatibility; Downgrade the Mellanox OFED drivers to the previous version.
C) Resource contention; Use 'perf or 'bpftrace' to profile the data plane processes and identify resource bottlenecks (CPU, memory, cache).
D) Network misconfiguration; Verify the MTU and QOS settings on the network interfaces.
E) Firmware corruption; Re-flash the BlueField DPIJ with the latest firmware image.

3. An NVIDIA-based AI server is experiencing intermittent network connectivity issues. You suspect a problem with the SFP28 transceiver connecting the server to the network. You want to use command-line tools to retrieve the DOM (Digital Optical Monitoring) information from the transceiver to diagnose the problem. Assuming you have appropriate network administration privileges, which command sequence is the MOST likely to provide the necessary DOM data (assuming 'eth0' is the interface)?

A)

B)

C)

D)

E)

4. You are deploying a cloud-native AI inference service using Kubernetes and NVIDIA GPUs. You need to ensure that GPU resources are efficiently allocated and monitored. Which of the following approaches is MOST effective for achieving this within the Kubernetes environment?

A) Overcommitting GPU resources and relying on the Kubernetes scheduler to handle potential out-of-memory (OOM) errors.
B) Relying solely on Kubernetes' default CPU and memory resource requests and limits, assuming GPU usage will be implicitly managed.
C) Deploying a dedicated monitoring agent on each node to track GPU utilization and manually adjusting pod resource requests based on these metrics.
D) Manually assigning specific GPU devices to pods using hostPath volumes and environment variables.
E) Using the NVIDIA Device Plugin for Kubernetes to advertise GPU resources and utilizing resource requests and limits to schedule pods on nodes with available GPUs.

5. Your AI infrastructure includes several NVIDIAAI 00 GPUs. You notice that the GPU memory bandwidth reported by 'nvidia-smi' is significantly lower than the theoretical maximum for all GPUs. System RAM is plentiful and not being heavily utilized. What are TWO potential bottlenecks that could be causing this performance issue?

A) The CPU is using older DDR4 memory with low bandwidth
B) Inefficient data loading from storage to GPU memory.
C) The NVIDIA drivers are not configured to enable peer-to-peer memory access between GPUs.
D) The GPUs are connected via PCle Gen3 instead of PCle Gen4.
E) Insufficient CPU cores assigned to the training process.

Solutions: