NVIDIA NCP-AII Questions Answers

The two most likely causes are network packet loss and thermal throttling. A performance drop after 48 hours of HPL burn-in is less likely to be a simple launch-time MPI configuration issue, because MPI affinity errors usually appear from the beginning of the run as consistently poor performance. A delayed degradation suggests the system changed state under sustained load. Thermal throttling is a common cause: after many hours, rack cooling imbalance, blocked airflow, high inlet temperature, or fan behavior can cause GPU clocks to drop. nvidia-smi dmon helps monitor GPU temperature, power, utilization, and clocks over time. Network packet loss is also likely in multi-node HPL because HPL depends on heavy communication across the InfiniBand fabric. Link errors, symbol errors, retransmissions, degraded cables, or congestion can reduce sustained performance. ibdiagnet is the correct fabric-level diagnostic tool to collect and analyze InfiniBand health, topology, counters, and link issues. Rebooting and reducing matrix size would hide the symptom rather than diagnose it. Correct burn-in practice is to preserve evidence, inspect thermal telemetry, review network diagnostics, and compare the affected node against healthy peers.

While the InfiniBand fabric is known for high bandwidth, its defining characteristic for AI workloads is ultra-low, sub-microsecond latency. When new nodes or switches are added, administrators must verify that the point-to-point latency meets the hardware specifications. The ib_write_lat utility is the standard micro-benchmark from the perftest suite used for this purpose. It measures the time it takes to complete an RDMA Write operation between two nodes. This tool is " verified " because it operates directly over the InfiniBand Verbs layer, bypassing the CPU overhead of the standard TCP/IP stack. Unlike tcpdump (Option A), which is used for packet capture, or ibdiagnet (Option C), which is used for fabric-wide discovery and error reporting, ib_write_lat provides a granular, nanosecond-level measurement of the link ' s responsiveness. In an AI cluster, even a small increase in latency can cause a " straggler " effect in distributed training, where all GPUs wait for the slowest link to complete a synchronization step.

When deploying NVIDIA vGPU on VMware ESXi, the NVIDIA driver provides several scheduling policies to determine how GPU physical resources are shared among multiple virtual machines. The default behavior is often the " Best Effort " scheduler, but for environments requiring predictable performance across all users, the " Equal Share " scheduler is preferred. This scheduler gives each vGPU an equal " time slice " of the physical GPU ' s engines. The configuration is managed via module parameters passed to the nvidia kernel driver during host boot. The specific registry key for this behavior is RmPVMRL. Setting RmPVMRL=0x01 enables the Equal Share scheduler (Option A). Conversely, 0x00 would revert to the default time-sliced behavior. It is critical to use system module parameters set to ensure the setting persists across reboots and is applied globally to the NVIDIA driver stack. This ensures that no single " noisy neighbor " VM can monopolize the GPU cycles, which is a common requirement in shared AI research labs or virtual desktop infrastructures where consistency is more important than raw peak throughput of a single task.

The correct answer is C because mdadm --examine and mdadm --detail directly inspect Linux software RAID metadata and array health. During DGX cluster deployment, local RAID storage must be verified before the node is released for AI workloads, logging, container images, datasets, scratch space, or checkpoint staging. A mounted filesystem only proves that the operating system can access the volume; it does not confirm that the RAID array is clean, synchronized, degraded, rebuilding, or free of disk failure events. FIO is useful for performance benchmarking, but speed and latency results alone do not prove RAID integrity. A degraded array can sometimes still deliver acceptable short-term performance while lacking redundancy or carrying latent errors. The mdadm --detail command reports array state, active devices, failed devices, rebuild progress, spare disks, and consistency status. The mdadm --examine command checks member-device metadata and helps confirm whether disks belong correctly to the array. In NVIDIA AI infrastructure bring-up, RAID health validation is part of server readiness because storage faults can cause job failures, lost logs, unstable container operation, or checkpoint corruption during production workloads.