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NVIDIA AI Infrastructure Sample Questions:

1. You're deploying a multi-GPU training job on a cluster using Slurm. You need to ensure that the GPUs allocated to the job are healthy and functioning correctly before the training starts. What's the MOST effective approach to pre-validate the GPU hardware?

A) Allocate all available GPUs to the job and assume they are healthy.
B) Monitor the GPU temperature using 'nvidia-smi' during the first few minutes of the training job.
C) Run a simple CUDA vector addition program on each GPU and check for errors.
D) Check the output of 'nvidia-smi' to ensure all GPUs are listed and have the expected memory.
E) Execute the NVIDIA Data Center GPU Manager (DCGM) diagnostic suite on the allocated GPUs.

2. You're deploying a BlueField-2 DPU in a cloud environment and need to ensure the integrity of the DPU's firmware. You want to verify that the firmware hasn't been tampered with. Which of the following methods provides the strongest level of assurance for firmware integrity?

A) Using a digitally signed firmware image and verifying the signature using NVIDIA's public key.
B) Checking the MD5 checksum of the firmware image against a known good value.
C) Checking the file size of the firmware image against a known good value.
D) Verifying the SHA256 checksum of the firmware image against a known good value provided by NVIDIA.
E) Comparing the firmware version reported by the DPU with the version listed in the NVIDIA release notes.

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

4. You observe high latency and low bandwidth between two GPUs connected via an NVLink switch. You suspect a problem with the NVLink link itself. Which of the following methods would be the most effective in diagnosing the physical NVLink link health?

A) Using 'iperf3' to measure network throughput between the servers.
B) Running a CUDA-aware memory bandwidth test specifically designed for NVLink.
C) Physically inspecting the NVLink cables for damage.
D) Using 'ping' to check network connectivity between the servers.
E) Examining system logs for NVLink-related error messages.

5. You're optimizing an AMD EPYC server with 4 NVIDIAAIOO GPUs for a large language model training workload. You observe that the GPUs are consistently underutilized (50-60% utilization) while the CPUs are nearly maxed out. Which of the following is the MOST likely bottleneck?

A) Insufficient CPU cores to prepare and feed data to the GPUs.
B) The PCle interconnect between the CPUs and GPUs is saturated.
C) The NCCL (NVIDIA Collective Communications Library) is not properly configured for inter-GPU communication.
D) The storage system (SSD/NVMe) is too slow, leading to data starvation.
E) The system RAM is too small, causing excessive swapping.

Solutions: