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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
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	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 4	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 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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Quiz 2026 NCP-AII: NVIDIA AI Infrastructure – High Pass-Rate Valid Exam Question

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NVIDIA AI Infrastructure Sample Questions (Q124-Q129):

NEW QUESTION # 124
You are deploying a multi-node A1 training cluster using Kubernetes, with each node equipped with multiple NVIDIA GPUs. You want to ensure that the Kubernetes scheduler is aware of the GPU resources available on each node and can efficiently allocate GPU-enabled pods to the appropriate nodes. Besides installing the NVIDIA Container Toolkit, what other components are essential for enabling GPU-aware scheduling in Kubernetes?

A. The Kubernetes Horizontal Pod Autoscaler (HPA).
B. The NVIDIA Device Plugin for Kubernetes.
C. The Kubernetes Resource Quota controller.
D. The NVIDIA GPU Operator.
E. The NVIDIA Fabric Manager

Answer: B,D

Explanation:
The NVIDIA Device Plugin for Kubernetes (A) is essential for advertising the GPU resources to the Kubernetes scheduler. It allows Kubernetes to understand that GPUs are available and track their usage. The NVIDIA GPU Operator (C) simplifies the deployment and management of NVIDIA drivers and other components required for GPU support in Kubernetes, including the device plugin. The Resource Quota controller (B) is useful for limiting resource consumption but doesn't directly enable GPU-aware scheduling. HPA (D) is used for autoscaling based on CPU or memory utilization, not GPU utilization. Fabric Manager is for managing GPU interconnect and not related.

NEW QUESTION # 125
Refer to the image:

Which MAC address should be used by the system administrator as the DPU's out-of-band management interface MAC address?

A. 0002C9270502
B. 0002C927050E
C. 0002C927050F
D. 0002C9270500

Answer: A

Explanation:
The label shows the base host MAC as 0002C9270500. The BlueField DPU out-of-band management interface uses the derived management MAC by incrementing the base host MAC, resulting in 0002C9270502.

NEW QUESTION # 126
You are building a Docker image for a deep learning application that requires an NVIDIA GPU. Which of the following instructions is the most efficient way to ensure the NVIDIA drivers are available within the container, assuming you are using the nvidia/cuda/' base image and want to minimize the image size?

A. COPY /usr/lib/nvidia /usr/local/nvidia/
B. FROM nvidia/cuda:ll .4.2-base-ubuntu20.04 RUN apt-get update && apt-get install -y -no-install-recommends nvidia-driver-470
C. FROM nvidia/cuda:ll .4.2-base-ubuntu20.04 AS builder RUN apt-get update && apt-get install -y -no-install-recommends software-properties-common RUN add-apt-repository ppa:graphics-drivers/ppa RUN apt-get update && apt-get install -y -no-install-recommends nvidia-driver-470 FROM ubuntu:20.04 COPY -from=builder /usr/lib/nvidia /usr/lib/ COPY -from=builder /usr/local/nvidia /usr/local/
D. RUN apt-get update && apt-get install -y nvidia-driver-470
E. Using 'nvidia/cuda' base image, the drivers are already included, so no further action is needed.

Answer: E

Explanation:
The Anvidia/cuda' base images provided by NVIDIA come pre-installed with the necessary NVIDIA drivers and CUDA toolkit. Therefore, you don't need to manually install or copy driver files. This is the most efficient method as it avoids unnecessary bloat in the Docker image. Options A, B, C and D all lead to redundant driver installations, increasing image size.

NEW QUESTION # 127
You are tasked with validating a newly installed NVIDIAAIOO Tensor Core GPU within a server. You need to confirm the GPU is correctly recognized and functioning at its expected performance level. Describe the process, including commands and tools, to verify the following aspects: 1) GPU presence and basic information, 2) PCle bandwidth and link speed, and 3) Sustained computational performance under load.

A. 1) Use 'nvidia-smi' for presence and basic info. 2) Use 'nvlink-monitor' for bandwidth/speed. 3) Run a CPU-bound benchmark to avoid GPU bottlenecks.
B. 1) Check BIOS settings for GPU detection. 2) Use 'Ispci -vv' to check PCle speed. 3) Run a PyTorch ImageNet training script.
C. 1) Use 'Ispci I grep NVIDIA' for presence, 'nvidia-smi' for basic info. 2) Use 'nvidia-smi -q -d pcie' for bandwidth/speed. 3) Run a TensorFlow ResNet50 benchmark.
D. 1) Use 'nvidia-smi' for presence and basic info. 2) PCle speed is irrelevant. 3) Run the 'nvprof profiler during a CUDA application.
E. 1) Use 'nvidia-smi' for presence and basic info. 2) Use 'nvidia-smi -q -d pcie' for bandwidth/speed. 3) Run a CUDA-based matrix multiplication benchmark (e.g., using cuBLAS) with increasing matrix sizes and monitor performance.

Answer: E

Explanation:
'nvidia-smi' is the primary tool for NVIDIA GPU information. 'nvidia-smi -q -d pcie' provides PCle details. A CUDA-based benchmark isolates GPU performance. Other options have elements of truth but aren't complete or optimally targeted (e.g., ResNet50 relies on other frameworks). Using a CPU-bound benchmark wouldn't test the GPU's capabilities.

NEW QUESTION # 128
You are tasked with installing a DGX A100 server. After racking and connecting power and network cables, you power it on, but the BMC (Baseboard Management Controller) is not accessible via the network. You have verified the network cable is connected and the switch port is active. What are the MOST likely causes and initial troubleshooting steps you should take?

A. The network switch port is not configured for the correct VLAN. Verify the switch port configuration to ensure it is on the same VLAN as the BMC.
B. The BMC IP address is not configured or is on a different subnet. Check the BMC's network configuration using the DGX's front panel or via serial console. Verify DHCP is enabled and functioning or manually configure a static IP address.
C. The BMC firmware is corrupted and needs to be reflashed using a USB drive. Check the DGX support site for the latest BMC firmware.
D. The BMC is faulty and needs to be replaced. Contact NVIDIA support for RMA.
E. The BMC is not powered on because the main power supply is faulty. Verify the power supply LEDs are lit and providing power to the system.

Answer: A,B

Explanation:
The most likely causes are network configuration issues (incorrect IP, subnet, or VLAN). The BMC requires a valid IP configuration and network connectivity to be accessible. While other options are possible, they are less common as initial causes.

NEW QUESTION # 129
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