TestsDumps NVIDIA NCP-AII Questions PDF Format

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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q175-Q180):

NEW QUESTION # 175
ClusterKit's NCCL bandwidth test shows 350 GB/s on a 400G InfiniBand fabric. How should this result be interpreted?

Answer: D

Explanation:
A 350 GB/s NCCL bandwidth result on a 400G InfiniBand fabric indicates strong collective communication performance. This is consistent with a healthy fabric and effective GPUDirect RDMA operation during ClusterKit validation.


NEW QUESTION # 176
A company deploys a large language model training cluster containing multiple NVIDIA HGX servers. During benchmarking, GPUs within each server communicate efficiently, but gradient synchronization across servers introduces significantly higher latency than expected. The infrastructure engineer needs to identify the component primarily responsible for optimizing GPU- to-GPU communication between different servers while maintaining maximum bandwidth and minimum latency.

Answer: C

Explanation:
NVSwitch accelerates communication among GPUs inside a single HGX server, but it does not extend across physical servers. Multi-node distributed training depends on the InfiniBand fabric and technologies such as GPUDirect RDMA to provide low-latency, high-bandwidth communication. PCIe switches remain local to the server, while MIG partitions GPUs rather than improving network communication.


NEW QUESTION # 177
One of the nodes in a cluster is not running as fast as the others and the system administrator needs to check the status of the GPUs on that system. What command should be used?

Answer: D

Explanation:
The nvidia-smi (NVIDIA System Management Interface) utility is the primary tool for monitoring and managing the state of NVIDIA GPUs. When a node exhibits "jitter" or lower performance compared to its peers in a cluster, nvidia-smi provides the necessary granular data to identify the bottleneck. It reports critical metrics such as GPU utilization percentages, memory usage, and, most importantly, "Clocks Throttle Reasons." If a GPU is running slower due to power capping, thermal issues, or a hardware error (like an uncorrectable ECC error), nvidia-smi will display this state immediately. While lspci (Option A) can confirm if the GPU is physically visible on the PCIe bus, it cannot provide any telemetry regarding its operational performance or health. iblinkinfo (Option D) is a network-level tool that only monitors InfiniBand link states.
In an AI infrastructure context, nvidia-smi is the first-line diagnostic to determine if a GPU is "healthy" and operating at its intended clock speeds.


NEW QUESTION # 178
You are training a deep neural network using NCCL to coordinate communication across four GPUs in a single node. During early performance testing, you notice inconsistent scaling and longer-than-expected training times, even though all GPUs are being used. Which strategy would most effectively improve NCCL efficiency and collective operation performance in this setting?

Answer: C

Explanation:
NCCL collective performance depends on balanced work across GPUs so that no GPU becomes a straggler during synchronization. Equalizing the batch portion per GPU keeps computation and communication aligned, improving scaling efficiency and reducing delays in collective operations.


NEW QUESTION # 179
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 especially damaging to East-West AI fabric performance because NCCL collective operations depend on reliable, high-throughput GPU-to-GPU communication. Even small packet- loss rates can trigger retries and pipeline stalls, causing latency spikes and reducing effective bandwidth across the fabric.


NEW QUESTION # 180
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