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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.

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NVIDIA AI Infrastructure Sample Questions (Q156-Q161):

NEW QUESTION # 156
An engineer needs to validate NVLink Switch functionality on a DGX H100 system with 8 GPUs. Which NCCL command verifies intra-node NVLink bandwidth?

A. all_reduce_perf -b 8 -e 16G -f 2 -g 4 with NCCL_TESTS_SPLIT="MOD 2"
B. all_reduce_perf -b 8 -e 16G -f 2 -g 8 with NCCL_TESTS_SPLIT="OR 0x7"
C. all_reduce_perf -b 8 -e 16G -f 2 -g 1 repeated 8 times
D. broadcast_perf -b 8 -e 16G -f 2 -g 8 without split configuration

Answer: B

Explanation:
The NVIDIA Collective Communications Library (NCCL) "Tests" are used to verify the maximum achievable bandwidth of the interconnects. On a DGX H100, the GPUs are connected via a dedicated high- bandwidthNVLink Switch fabric(NVLink 4), which provides significantly higher throughput than PCIe. To validate the intra-node (within a single server) performance, the all_reduce_perf test is used. The command in Option D is specifically designed to stress all 8 GPUs (-g 8) across a wide range of message sizes (8 bytes to
16G). The use of the environment variable NCCL_TESTS_SPLIT with the bitwise "OR" or "AND" masks allows the engineer to isolate specific traffic patterns or groups of GPUs to ensure the NVLink switches are distributing the load evenly. For a standard 8-GPU H100 tray, achieving a "Bus Bandwidth" of ~450 GB/s to
900 GB/s (depending on the precision and message size) confirms that the NVLink fabric is operating at its theoretical peak. Using only 4 GPUs (Option B) or 1 GPU (Option C) would not provide a complete picture of the NVLink switch bisection bandwidth.

NEW QUESTION # 157
Refer to the exhibit. What is the maximum bandwidth supported by Direct Attach Copper (DAC) LinkX cables?

A. 400 Gbps
B. 100 Gbps
C. 50 Gbps
D. 200 Gbps

Answer: A

Explanation:
NVIDIA LinkX Direct Attach Copper cables support high-speed Ethernet and InfiniBand connectivity up to 400 Gbps, making them suitable for dense GPU cluster interconnects and short-reach data center links.

NEW QUESTION # 158
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?

A. Increase the fan speed to maximum and check whether the performance improves.
B. Run a deep learning workload to stress test the GPUs and check whether the issue persists.
C. Power drain then restart the DGX and check if the performance degradation resolves.
D. Check the NVIDIA System Management Interface (nvidia-smi) for GPU status and temperatures.

Answer: D

Explanation:
nvidia-smi is the appropriate first diagnostic tool for checking GPU health, utilization, power, temperature, throttling, and error indicators. Since degraded performance and loud fans may point to thermal or GPU hardware issues, reviewing GPU status and temperatures helps identify the fault condition before taking corrective action.

NEW QUESTION # 159
After initial setup and health checks, the DGX H100 system administrator wants to verify that containers can access GPUs before running production workloads. Which method is recommended for this validation?

A. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 systemctl
B. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 ls-la
C. sudo docker run --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi
D. sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi

Answer: D

Explanation:
Running a CUDA container with --gpus all and executing nvidia-smi is the standard validation that Docker, the NVIDIA container runtime, drivers, and GPU access are working correctly before production workloads are launched.

NEW QUESTION # 160
On a DCX, a system administrator needs to monitor PSU, CPU Utilization, GPU Utilization, RAID, and Memory Utilization. What single NVIDIA tool should be used?

A. NVSM
B. MOFED
C. SMI
D. DCGM

Answer: A

Explanation:
NVSM is the NVIDIA System Management tool for DGX systems and provides system-level monitoring across components such as power supplies, CPU utilization, GPU utilization, RAID/storage status, and memory utilization from a single management interface.

NEW QUESTION # 161
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