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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
  • 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
  • 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 5
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q118-Q123):

NEW QUESTION # 118
A system engineer needs to set the vGPU scheduling behavior for all GPUs to share the scheduling equally with the default time slice length. What command should be used?

Answer: A

Explanation:
When deploying NVIDIA vGPU on VMware ESXi, the NVIDIA driver provides several scheduling policies to determine how GPU physical resources are shared among multiple virtual machines. The default behavior is often the "Best Effort" scheduler, but for environments requiring predictable performance across all users, the "Equal Share" scheduler is preferred. This scheduler gives each vGPU an equal "time slice" of the physical GPU's engines. The configuration is managed via module parameters passed to the nvidia kernel driver during host boot. The specific registry key for this behavior is RmPVMRL. Setting RmPVMRL=0x01 enables the Equal Share scheduler (Option A). Conversely, 0x00 would revert to the default time-sliced behavior. It is critical to use system module parameters set to ensure the setting persists across reboots and is applied globally to the NVIDIA driver stack. This ensures that no single "noisy neighbor" VM can monopolize the GPU cycles, which is a common requirement in shared AI research labs or virtual desktop infrastructures where consistency is more important than raw peak throughput of a single task.


NEW QUESTION # 119
A system administrator receives an alert about a potential hardware fault on an NVIDIA DGX A100. The GPU performance seems degraded, and the system fans are operating loudly. What step should be recommended to identify and troubleshoot the hardware fault?

Answer: D

Explanation:
When a DGX system exhibits high fan speeds and performance degradation, it is typically engaging in Thermal Throttling. High-performance GPUs like the A100 or H100 will automatically reduce their clock speeds (and thus performance) if they exceed safe temperature thresholds. The first and most critical diagnostic step is to run nvidia-smi. This utility provides immediate, real-time telemetry on GPU temperatures, power draw, and " Clocks Throttle Reasons. " By reviewing the output, an administrator can see if " Thermal " is listed as the reason for reduced clocks. This identifies whether the issue is environmental (blocked airflow/hot aisle temperature) or hardware-specific (a failed GPU thermal interface or a dead internal fan). Running more workloads (Option A) would exacerbate the heat, while a power drain (Option C) is a " last resort " that doesn ' t provide diagnostic data. nvidia-smi provides the evidentiary data needed to determine if an RMA (Return Merchandise Authorization) is required for the GPU tray.


NEW QUESTION # 120
An infrastructure engineer in an AI factory has successfully replaced a power supply unit on an NVIDIA DGX H100. After installation, both the IN and OUT LEDs on the new power supply illuminate solid green.
Which NVSM CLI command should the engineer use to quickly verify the overall system status and ensure it is operating as expected?

Answer: C

Explanation:
The NVIDIA System Management (NVSM) tool is the definitive CLI utility for monitoring the health of DGX platforms. While replacing a PSU (Power Supply Unit) is a common maintenance task, verifying that the new component is correctly integrated into the system's health model is mandatory. While nvsm show power would provide specific data regarding wattage and voltage for the PSU, the most comprehensive way to ensure the replacement hasn ' t caused secondary issues or that the system hasn ' t remained in a " Degraded
" state is to run nvsm show health. This command performs a global check across all subsystems: GPUs, NVLink switches, storage, fans, and power. If the PSU replacement was successful and the system is back to full redundancy, nvsm show health will return a " Healthy " status. In an AI factory setting, where DGX H100 nodes pull significant power, ensuring that all 6 PSUs (in an N+N or N+1 configuration) are not only physically green but logically acknowledged by the Baseboard Management Controller (BMC) is critical for preventing unexpected shutdowns during high-load training iterations.


NEW QUESTION # 121
Consider the following Dockerfile snippet:

This Dockerfile is used to build a deep learning application. After building and running a container from this image, you observe that the application is not detecting the GPU. You have verified that the NVIDIA Container Toolkit is installed and configured correctly on the host. What is the most likely reason for this issue?

Answer: A

Explanation:
The 'docker run' command must include the =gpus all' (or equivalent) flag to explicitly request GPU resources for the container (C). The base image 'nvidia/cuda:ll .6.2-base-ubuntu20.04' provides the CUDA runtime, but the NVIDIA Container Toolkit on the host handles the GPU device mapping. The application code (B) doesn't need to explicitly request GPUs; the CUDA runtime will handle that. 'nvidia-pyindex' (D) is related to package management, not GPU detection. The Dockerfile includes a specific CUDA version that mitigates version differences (E). The base image does not include the Container Toolkit, which is installed on the HOST.


NEW QUESTION # 122
A cluster administrator needs to validate transceiver firmware versions across 200 ports using UFM. Which GUI-based method provides a consolidated view?

Answer: C

Explanation:
Managing a large-scale AI fabric requires centralized visibility into the physical layer. The NVIDIAUnified Fabric Manager (UFM)provides a comprehensive Dashboard for InfiniBand networks. To check transceiver firmware-which is critical for ensuring feature parity and stability across the fabric-the administrator can use the UFM Enterprise GUI. By navigating to the "Devices" section and selecting a specific switch, the
"Cables" tab will aggregate telemetry for every occupied port. This view displays the manufacturer, part number, and the specific firmware version of the transceivers (LinkX) or Active Optical Cables (AOC). This consolidated view is far more efficient than manual CLI queries (Option C) for 200+ ports. Maintaining uniform firmware across transceivers ensures that optimizations like Adaptive Routing and Congestion Control perform consistently across the entire 400G or 200G fabric.


NEW QUESTION # 123
......

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