Real NVIDIA NCP-AII Exam Questions in PDF Format

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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 2
  • System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC
  • OOB
  • TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 3
  • Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm
  • Enroot
  • Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
Topic 4
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5
  • Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD
  • Intel servers and storage.

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NVIDIA AI Infrastructure Sample Questions (Q123-Q128):

NEW QUESTION # 123
You are configuring a network bridge on a Linux host that will connect multiple physical network interfaces to a virtual machine. You need to ensure that the virtual machine receives an IP address via DHCP. Which of the following is the correct command sequence to create the bridge interface 'br0', add physical interfaces 'eth0' and 'eth1' to it, and bring up the bridge interface? Assume the required packages are installed. Consider using 'ip' command.

Answer: D

Explanation:
Option D is the correct sequence using the Sip' command. First, create the bridge ' bro'. Then, add the physical interfaces 'eth0 and "eth1' as slaves to the bridge. Next, bring up the physical interfaces. After that, bring up the bridge interface . Finally, use "dhclient bro to obtain an IP address for the bridge via DHCP. Option C is the old way, using 'brctr and 'ifconfig', which are deprecated. The others lack the crucial step of bringing up the bridge after attaching the physical interfaces and before running 'dhclient'.


NEW QUESTION # 124
During a 72-hour HPL burn-in test on a DGX H100 cluster, one node shows a 15% performance drop after 48 hours. What are the two most likely causes and diagnostic steps?
Pick the 2 correct responses below.

Answer: B,C

Explanation:
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.


NEW QUESTION # 125
An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled. What are the THREE most likely root causes of these crashes?

Answer: C,D,E

Explanation:
GPU memory errors (B) are a strong possibility, as CPU-based tests don't test GPU memory directly. Insufficient airflow (C) is likely due to the passive cooling, leading to thermal instability. A faulty PSU (D) can cause random crashes under load due to power fluctuations. Driver incompatibility (A) is less likely to cause random crashes after initial setup, and network congestion (E) usually results in training slowdowns rather than system crashes.


NEW QUESTION # 126
A team is validating a DGX BasePOD deployment. Using cmsh, they run a command to check GPU health across all nodes. What indicates that the system is ready for AI workloads?

Answer: D

Explanation:
In an NVIDIA DGX BasePOD or SuperPOD environment, "Cluster Health" is a binary state: either the entire fabric and all compute resources are ready, or the cluster is considered degraded. Using the Bright Cluster Manager (BCM) shell (cmsh), administrators can aggregate telemetry from every node in the cluster. For a system to be considered "Production Ready," every single GPU across the multi-node deployment must report a status of Health = OK. This verification ensures that the hardware is communicating correctly over the PCIe bus, the NVLink fabric is initialized, and no ECC (Error Correction Code) memory errors are present. If even a single GPU in a 32-node cluster is unhealthy, collective communication libraries like NCCL may hang or experience significant performance penalties during "All-Reduce" operations, as the entire job typically scales to the speed of the slowest/unhealthiest component. Therefore, seeing Status_Health = OK for every device is the mandatory exit criterion for the bring-up phase.


NEW QUESTION # 127
Which of the following commands or tools can be used to verify the NVIDIA driver version and the CUDA version installed on a Linux system?

Answer: A,C,D,E

Explanation:
'nvidia-smi' provides detailed information about the NVIDIA driver version and GPU status. 'nvcc -version' shows the CUDA compiler version. 'cat Iproc/driver/nvidia/version' (if the file exists) displays the driver version. 'modinfo nvidia' will display the version of the loaded kernel module. only shows the presence of NVIDIA hardware, not the driver or CUDA version.


NEW QUESTION # 128
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