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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA-Certified Professional AI Infrastructure Exam
Exam Number:	NCP-AII
Real Exam Qty:	60-70
Passing Score:	Pass/Fail only, no numerical score
Exam Price:	$400 USD
Exam Format:	Multiple-choice questions, Scenario-based items
Exam Duration:	120 minutes
Related Certifications:	NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Operations (NCP-AIO)
Certificate Validity Period:	2 years
Available Languages:	English
Recommended Training:	NVIDIA AI Infrastructure Training
Exam Registration:	NVIDIA Certification Portal
Sample Questions:	NVIDIA NCP-AII Sample Questions
Exam Way:	Online remote proctored or onsite at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended 2–3 years of experience in data center infrastructure, Linux administration, and NVIDIA hardware/software environments
Official Syllabus URL:	https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-professional/

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5	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.

NVIDIA AI Infrastructure Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are managing a Kubernetes cluster with NVIDIA GPUs and want to automatically scale your A1 inference deployments based on GPU utilization. Which of the following tools and configurations would you use to implement Horizontal Pod Autoscaling (HPA) based on GPU metrics?

A. Configuring the HPA based on memory utilization.
B. Using the NVIDIA Data Center GPU Manager (DCGM) exporter to expose GPU metrics to Prometheus, and configuring the HPA to scale based on these metrics.
C. Manually adjusting the number of replicas in the deployment based on observed GPIJ utilization.
D. Using the standard Kubernetes HPA with CPU utilization as the scaling metric, assuming GPU utilization is correlated with CPU usage.
E. Using the Kubernetes Resource Metrics API to directly access GPU utilization metrics and configuring the HPA accordingly.

Answer: B

Explanation:
The NVIDIA DCGM exporter provides a way to expose GPU metrics to Prometheus, which can then be used by the Kubernetes HPA to automatically scale deployments based on actual GPU utilization. The standard Kubernetes HPA does not natively support GPU metrics. Other options are either inaccurate or manual.

NEW QUESTION # 17
After ClusterKit reports "GPU-Host latency exceeds threshold", which NVIDIA diagnostic tool should be used to isolate hardware faults?

A. ib_write_bw to measure InfiniBand bandwidth between nodes
B. Re-run ClusterKit with --stress=gpu -Y 60 to extend test duration
C. nvidia-smi topo -m to inspect GPU topology connections
D. DCGM Diags dcgmi diag -r 2

Answer: D

Explanation:
DCGM Diagnostics with dcgmi diag -r 2 is the NVIDIA hardware diagnostic tool used to isolate GPU-related hardware faults after performance or latency anomalies are detected. It performs deeper GPU health validation beyond topology inspection or workload reruns.

NEW QUESTION # 18
A research organization deploys NVIDIA BlueField DPUs in an AI cluster to improve infrastructure efficiency. Shortly after deployment, administrators observe that CPU utilization on compute nodes decreases during storage and network-intensive workloads while GPU utilization remains unchanged. Which DPU capability most directly explains this improvement?

A. Replacing the InfiniBand fabric with PCIe communication
B. Offloading infrastructure services such as networking, storage, and security from the host CPU
C. Executing CUDA kernels on behalf of GPUs
D. Increasing GPU memory capacity through memory pooling

Answer: B

Explanation:
BlueField DPUs are designed to offload infrastructure functions--including networking, storage virtualization, encryption, firewall processing, and telemetry--from the host CPU. This frees CPU resources for application execution while maintaining GPU performance. DPUs do not execute CUDA workloads, expand GPU memory, or replace the network interconnect.

NEW QUESTION # 19
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. broadcast_perf -b 8 -e 16G -f 2 -g 8 without split configuration
C. all_reduce_perf -b 8 -e 16G -f 2 -g 1 repeated 8 times
D. all_reduce_perf -b 8 -e 16G -f 2 -g 8 with NCCL_TESTS_SPLIT="OR 0x7"

Answer: D

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 # 20
A cluster administrator needs to validate transceiver firmware versions across 200 ports using UFM. Which GUI-based method provides a consolidated view?

A. Export all switch logs and grep for 'FW Version".
B. Navigate to 'Devices" > select a switch > "Cables' tab to see ASIC firmware and transceiver versions.
C. Run mlxlink -d lid-<LID> -m on each port manually.
D. Use "Topology' view to visually inspect cable icons.

Answer: B

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 # 21
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