Quiz 2026 NVIDIA The Best Valid NCP-AII Study Plan

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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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 (Q106-Q111):

NEW QUESTION # 106
You are deploying a multi-node NVIDIA GPU cluster for distributed deep learning. Each node has a different ambient operating temperature due to varying airflow patterns within the data center. To ensure optimal performance and longevity of the GPUs across all nodes, which approach is MOST effective for managing GPU power limits?

Answer: A

Explanation:
Option C, using DCGM for dynamic power management, is the most effective approach. It allows for per-GPU power limit adjustments based on real-time conditions, optimizing performance while ensuring thermal safety and longevity across nodes with different operating temperatures. A uniform power limit (A) might be too restrictive for some nodes or insufficient for others. Disabling power capping (B) risks overheating and damage. Default settings (D) may not be optimal. Manually adjusting fan speeds (E) can help, but doesn't address power limits directly.


NEW QUESTION # 107
After replacing a GPU in a multi-GPU server, you notice that the new GPU is consistently running at a lower clock speed than the other GPUs, even under load. *nvidia-smi' shows the 'Pwr' state as 'P8' for the new GPU, while the others are at 'PO'. What is the MOST probable cause?

Answer: B

Explanation:
A GPU stuck in the 'P8' power state indicates that it's not drawing the power it needs to operate at full performance. Insufficient power delivery is the most likely cause. While the new GPU could potentially be overheating or requiring a firmware update, checking power connections and PSU capacity is the first step. Comparing the new GPU's model with the others is also useful, but 'P8' state strongly suggests a power issue. Driver issues are less likely to cause a specific 'P8' state; they typically result in more general performance problems.


NEW QUESTION # 108
A cluster administrator is preparing to update the firmware on a DGX H100 system, including the GPU tray (baseboard). What is the correct sequence of steps to perform a safe and successful firmware upgrade?

Answer: B

Explanation:
Updating firmware on an NVIDIA DGX H100 is a multi-stage process that requires careful orchestration to prevent hardware corruption. The first and most critical step is to ensure no workloads are running (stopping all GPU activity) to avoid conflicts during the flashing process. The standard NVIDIA procedure begins with updating and rebooting theBaseboard Management Controller (BMC). This is because the BMC manages the power sequencing and communication for all other trays; having the latest management logic active is a prerequisite for the subsequent steps. Once the BMC is updated and back online, the administrator proceeds with the motherboard and GPU tray updates. However, these updates are staged in flash memory and often do not "take effect" until the hardware undergoes acold reset(removing power completely). This physical or logical power cycle forces the various CPLDs and silicon root-of-trust modules to boot from the newly written firmware images. Finally, the administrator must verify completion using tools like nvsm show health or the BMC dashboard to ensure all components report the target versions and a "Healthy" status. Skipping the BMC update first (Option C) or the cold reset (Option B) can lead to mismatched firmware states that may cause system instability or boot failures.


NEW QUESTION # 109
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: B,D

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 # 110
An administrator needs to perform a comprehensive pre-production stress test on a DGX H100 system. Which command validates GPU, CPU, memory, and storage components while following NVIDIA's recommended procedure?

Answer: A

Explanation:
The correct command is sudo nvsm stress-test --force. NVIDIA recommends using NVSM for the DGX pre- flight stress test before putting a DGX H100 system into production or after servicing. The documented NVSM stress test can run checks across supported components, including GPUs, CPU, memory, and storage, and the recommended command for all supported components is sudo nvsm stress-test --force. nvidia-smi -q provides detailed GPU information, but it does not execute a full platform stress test. The Linux stress command can load CPU and I/O subsystems, but it is generic and does not validate the DGX platform using NVIDIA's health model. gpu_burn may stress GPUs, but it does not cover CPU, system memory, storage, and DGX-specific platform checks in the recommended way. During server bring-up, NVSM is preferred because it understands DGX hardware components and can identify platform health issues before the node is released to production workloads.


NEW QUESTION # 111
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