NVIDIA NCP-AII Web-Based Practice Exam Questions

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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
  • 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.
Topic 3
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 4
  • 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 5
  • 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.

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Hot NCP-AII Answers Real Questions | Professional NVIDIA NCP-AII Reliable Exam Dumps: NVIDIA AI Infrastructure

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NVIDIA AI Infrastructure Sample Questions (Q118-Q123):

NEW QUESTION # 118
After Spectrum-X fabric deployment, NCCL tests show intermittent latency spikes. Which network condition most severely impacts East-West bandwidth?

Answer: D

Explanation:
Packet loss is the most severe condition because NCCL collective communication depends on predictable, reliable East-West data movement between GPUs. In Spectrum-X Ethernet fabrics, AI workloads rely on RoCE, congestion control, proper QoS, and low-loss behavior to keep GPU communication pipelines moving.
Even small amounts of packet loss can cause retransmissions, stalled collectives, increased tail latency, and reduced effective bandwidth. NCCL all-reduce and related collectives are synchronized operations, so one delayed flow or rank can slow the entire job. Transceiver firmware mismatches are serious and can contribute to instability, but the direct condition most damaging to bandwidth is actual packet loss. A 400G port running at 70% utilization is not automatically a problem if traffic is balanced and lossless behavior is maintained.
Jitter below 5 ps with consistent latency is not a severe issue; it suggests stable timing behavior. In post- deployment troubleshooting, engineers should inspect switch counters, RoCE congestion statistics, ECN/PFC behavior, retransmissions, packet drops, and NCCL logs to determine whether the fabric is causing the observed latency spikes.


NEW QUESTION # 119
A company has a registered NGC account and their server has NGC CLI installed. What step should be taken first to gain access to NGC?

Answer: C

Explanation:
The NVIDIA GPU Cloud (NGC) is the central repository for AI-optimized containers, pre-trained models, and specialized SDKs. To interact with the NGC registry via the command line, the ngc CLI must be authenticated to the user's account. The command ngc config set is the verified first step to configure these credentials. When this command is executed, the user is prompted to provide their API Key, which is generated from the NGC web portal. This configuration process creates a local config file (typically in ~/.ngc
/config) that stores the authentication token, the preferred organization, and the team settings. Without running ngc config set, the CLI cannot authenticate requests to pull private containers or upload models. ngc init (Option B) is not a standard configuration command for the current NGC CLI architecture, and ngc config get (Option A) is only useful for viewing an existing configuration that has already been established.


NEW QUESTION # 120
An organization deploys a Kubernetes AI platform using GPU Operator. Several GPU nodes appear in the cluster, but containers requesting GPU resources remain in a Pending state. The administrator verifies that NVIDIA drivers are correctly installed. Which missing component is the most likely cause?

Answer: C

Explanation:
The NVIDIA Device Plugin advertises GPU resources to Kubernetes so that the scheduler can allocate GPUs to pods. Without it, GPUs remain invisible to Kubernetes even if drivers are installed correctly. DCGM Exporter provides monitoring, Node Feature Discovery adds hardware labels, and CUDA Toolkit inside containers does not expose GPU resources.


NEW QUESTION # 121
You have a BlueField-2 DPU running Ubuntu. After upgrading the MLNX OFED drivers, the DPU fails to boot properly. You are presented with a GRUB prompt. Which sequence of commands is most likely to help you boot into a working kernel?

Answer: A

Explanation:
When a boot fails after a driver upgrade, it's often because the new kernel/initrd is faulty. The most likely solution is to boot into the previous , known-good kernel. Option C attempts this by specifying '-old' in the kernel and initrd filenames, attempting to load the older, hopefully functional, kernel. It assumes the root partition is /dev/sda2 but is booting an older kernel. Option A uses '$(uname -r)' which likely resolves to the problematic kernel that caused the failure. Option B lacks version information and may also load a broken kernel. Option D uses /dev/nvme0n1p2, which might be incorrect for the root partition on the BlueField. Option E will try to load grub normally, which might fail again.


NEW QUESTION # 122
What command sequence is used to identify the exact name of the server that runs as the master SM in a multi-node fabric?

Answer: B

Explanation:
sminfo identifies the active master subnet manager and provides its LID. Using smpquery ND against that LID retrieves the node description, which includes the exact server name running the master SM.


NEW QUESTION # 123
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