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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 2
  • 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 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
  • 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
  • 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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NCP-AII Valid Test Papers & Latest NCP-AII Exam Topics

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NVIDIA AI Infrastructure Sample Questions (Q89-Q94):

NEW QUESTION # 89
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?

Answer: C

Explanation:
The error message GPU has fallen off the bus strongly suggests a hardware-related issue with the GPU's connection to the motherboard or its power supply. Insufficient power, a loose riser cable, driver bugs and overclocking can all lead to this. A faulty CPU, while capable of causing system instability, is less directly related to the GPIJ falling off the bus and therefore the least likely cause in this specific scenario.


NEW QUESTION # 90
If two ports must be connected, but one is SFP and one is QSFP, for example, to connect a 25 GbE Host Channel Adapter to a QSFP port capable of both 100 GbE and 25 GbE, which solution would best meet this requirement?

Answer: A

Explanation:
A QSA adapter is the correct solution when an SFP-based device must connect to a QSFP port that supports the required speed mode. QSA stands for QSFP-to-SFP adapter. It allows an SFP or SFP28 transceiver or cable to be inserted into a QSFP or QSFP28 cage, assuming the switch port supports operation at the lower target speed, such as 25 GbE. This is useful in mixed-speed NVIDIA networking environments where a 25 GbE Host Channel Adapter must connect to a switch port that is physically QSFP but electrically capable of
25 GbE operation. SFP connectors alone do not solve the mechanical mismatch because they cannot directly fit into a QSFP cage. An SFP-to-1G BASE-T adapter is intended for 1 GbE copper RJ45 connectivity and does not meet a 25 GbE requirement. A QSFP-to-QSFP DAC cable also fails because the host side is SFP- based. During physical-layer management, engineers must confirm port capability, supported cable type, transceiver compatibility, link speed, and firmware support to avoid link-down conditions during cluster bring- up.


NEW QUESTION # 91
You are managing a cluster of servers running Docker and NVIDIA GPUs. You want to monitor the GPU utilization of all Docker containers running on the cluster in real-time. Which tools or techniques could you use to achieve this?

Answer: A,B

Explanation:
DCGM exporter integrated with Prometheus and Grafana is a robust solution for real-time monitoring of GPU metrics across a cluster (B). DCGM provides detailed GPU metrics, and Prometheus/Grafana offers excellent visualization capabilities. NVML (D) is a low-level API that allows you to directly query GPU information, providing flexibility for custom monitoring solutions. While 'nvidia-smi' (A) can be used, it's not ideal for cluster-wide monitoring. 'docker stats' (C) does not provide GPU utilization metrics directly. NGC (E) offers a container registry, but not built-in cluster-wide GPU monitoring.


NEW QUESTION # 92
Which function is used to collect the cluster counters information?

Answer: D

Explanation:
GM is the function used to collect cluster counters information, aggregating fabric-level counter data for cluster monitoring and analysis.


NEW QUESTION # 93
An enterprise deploys NVIDIA Base Command Manager to administer a newly built AI cluster.
The infrastructure team wants to minimize manual provisioning whenever additional compute nodes are installed. Which capability of Base Command Manager most directly supports this objective?

Answer: C

Explanation:
Base Command Manager provides centralized cluster deployment, operating system provisioning, software image management, health monitoring, and lifecycle automation. These capabilities simplify scaling AI infrastructure by reducing manual configuration effort. It does not retrain AI models, overclock hardware, or orchestrate multi-cloud workload migration.


NEW QUESTION # 94
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