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NVIDIA AI Infrastructure Sample Questions:

1. You're deploying a distributed training workload across multiple NVIDIAAIOO GPUs connected with NVLink and InfiniBand. What steps are necessary to validate the end-to-end network performance between the GPUs before running the actual training job? (Select all that apply)

A) Run NCCL tests (e.g., to measure NVLink bandwidth and latency between GPUs on the same node.
B) Ping all nodes to confirm basic network connectivity
C) Use 'ibstat' to verify the status and link speed of the InfiniBand interfaces on each node.
D) Employ 'iperf3' or 'nc' to measure TCP/UDP bandwidth between nodes over the InfiniBand network.
E) Manually inspect the physical cabling of NVLink bridges and InfiniBand connections.

2. You are configuring a RoCEv2 (RDMA over Converged Ethernet) network using BlueField-2 DPUs. You are observing packet loss and performance degradation. You suspect that Congestion Control is not working correctly. What configuration parameter most directly impacts RoCEv2 congestion control behavior?

A) The number of RDMA queues configured on the DPU.
B) PFC (Priority Flow Control) configuration on the switch ports.
C) MTU size on the RoCEv2 interfaces.
D) The IOMMIJ configuration for the DPU.
E) ECN (Explicit Congestion Notification) configuration on the switch ports and DPU interfaces.

3. An A1 server exhibits frequent kernel panics under heavy GPU load. 'dmesg' reveals the following error: 'NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.' Which of the following is the least likely cause of this issue?

A) A driver bug in the NVIDIA drivers, leading to GPU instability.
B) Insufficient power supply to the GPIJ, causing it to become unstable under load.
C) Overclocking the GPU beyond its stable limits.
D) A faulty CPU.
E) A loose or damaged PCle riser cable connecting the GPU to the motherboard.

4. An NVIDIA DGX server with 8 GPUs is experiencing performance issues during a distributed deep learning training run. You suspect a problem with the GPU interconnects. You have already confirmed that NVLink is active. What is the most thorough approach to diagnose potential bandwidth or latency bottlenecks in the GPU-to-GPlJ communication paths?

A) Use 'nvidia-smi topo -m' to visualize the GPU topology and check the reported link speeds. Any links with significantly lower speeds are suspect.
B) Run NCCL all-reduce benchmarks (e.g., using the NCCL tests) to measure the actual communication bandwidth between all pairs of GPUs. Compare the results to expected theoretical peak bandwidth.
C) All of the above
D) Examine the output of 'dmesg' for any NVLink-related error messages or warnings.
E) Monitor GPU utilization with 'nvidia-smi' during the training run. Uneven utilization across GPUs indicates a potential communication bottleneck.

5. You have a Kubernetes cluster with nodes running different versions of the NVIDIA driver. You need to ensure that your containerized AI applications are always compatible with the specific driver version running on the node where they are scheduled. How can you achieve this driver version compatibility in a cloud-native way?

A) Manually create different container images for each driver version and use node selectors to schedule the correct image on the appropriate nodes.
B) Use the NVIDIA driver capabilities to detect the driver version at runtime and dynamically load the correct libraries.
C) Implement a webhook that inspects the node labels and injects the appropriate NVIDIA libraries into the pod at runtime.
D) Use the NVIDIA Operator to automatically manage driver installations and updates on the nodes, ensuring a consistent driver version across the cluster.
E) Use a shared volume to mount drivers into a container.

Solutions: