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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA Certified Professional – AI Infrastructure
Exam Number:	NCP-AII
Exam Price:	$195 USD
Exam Format:	Multiple Choice, Multiple Select
Available Languages:	English
Passing Score:	700 (scale of 0-1000)
Real Exam Qty:	50
Exam Duration:	90 minutes
Related Certifications:	NCP-AI NCP-DES
Certificate Validity Period:	2 years
Sample Questions:	NVIDIA NCP-AII Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	Recommended: hands-on experience with NVIDIA AI infrastructure products; basic knowledge of Linux, networking, and data center operations
Official Syllabus URL:	https://www.nvidia.com/en-us/certifications/ncp-ai-infra/

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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
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	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 3	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 4	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 5	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.

NVIDIA AI Infrastructure Sample Questions (Q16-Q21):

NEW QUESTION # 16
An engineer needs to verify NVLink isolation on a single node with 8 GPUs. Which NCCL test configuration stresses switch bisection bandwidth?

A. Use all_reduce_perf -b 8 -e 16G -f2 -g 8 without splits
B. Use reduce_scatter_pref -b 8 -e 16G -f2 -g 4
C. Use all_reduce_perf -b 8 -e 16G -f2 -g 8 with NCCL_TESTS_SPLIT= "AND 0x1"
D. Use NCCL_TESTS_SPLIT= "DIV 8" with point-to-point tests

Answer: C

Explanation:
Using all_reduce_perf across all 8 GPUs with NCCL_TESTS_SPLIT="AND 0x1" separates GPUs into traffic groups that exercise cross-switch communication paths, making it suitable for stressing NVLink switch bisection bandwidth and validating isolation behavior within the node.

NEW QUESTION # 17
A System Administrator needs to change the scheduling behavior of a single GPU to use a fixed share scheduler. What command achieves this?

A. nvidia-smi -i 0 -mig 1
B. esxcli system module parameters set -m nvidia -p
C. esxcli -i 0 -mig 18
D. mixconfig -d /dev/mst/mt4123_pciconf0 set LINK_TYPE_P1=2

Answer: B

Explanation:
Fixed-share vGPU scheduling is configured through the NVIDIA driver module parameters on ESXi using esxcli system module parameters set -m nvidia -p .... The parameter must specify the fixed-share scheduler value, typically using NVreg_RegistryDwords with the appropriate RmPVMRL setting.

NEW QUESTION # 18
You are responsible for ensuring interoperability between AI applications deployed across a diverse IT landscape, including an on-premises data center equipped with NVIDIA GPUs and multiple cloud platforms from different vendors. These environments need to support complex AI workflows that involve large-scale data processing, real-time analytics, and machine learning model training. To maintain consistent performance and flexibility, which strategy should you prioritize?

A. Ensure that all environments use compatible storage protocols and APIs, such as NFS or S3, to facilitate data exchange and integration across platforms.
B. Implement a multi-cloud strategy that leverages native storage solutions in each cloud platform while using middleware to ensure interoperability and data consistency.
C. Focus on increasing network bandwidth between locations to reduce latency and improve data transfer speeds.
D. Choose a vendor and standardize on one storage solution across the environments to simplify management to improve interoperability

Answer: A

Explanation:
Compatible storage protocols and APIs such as NFS and S3 allow AI applications, data pipelines, and analytics workflows to access and exchange data consistently across on-premises GPU infrastructure and multiple cloud platforms. This supports interoperability, portability, and flexible integration across diverse environments.

NEW QUESTION # 19
After Spectrum-X fabric deployment, NCCL tests show intermittent latency spikes. Which network condition most severely impacts East-West bandwidth?

A. Multiple transceiver firmware mismatches.
B. Jitter below 5 ps with consistent latency.
C. Packet loss greater than 0.001% causing NCCL pipeline stalls.
D. 400G port utilization at 70% on several nodes during tests.

Answer: C

Explanation:
Packet loss is the most severe condition because NCCL collective communication depends on predictable, reliable East-West data movement between GPUs. In Spectrum-X Ethernet fabrics, AI workloads rely on RoCE, congestion control, proper QoS, and low-loss behavior to keep GPU communication pipelines moving.
Even small amounts of packet loss can cause retransmissions, stalled collectives, increased tail latency, and reduced effective bandwidth. NCCL all-reduce and related collectives are synchronized operations, so one delayed flow or rank can slow the entire job. Transceiver firmware mismatches are serious and can contribute to instability, but the direct condition most damaging to bandwidth is actual packet loss. A 400G port running at 70% utilization is not automatically a problem if traffic is balanced and lossless behavior is maintained.
Jitter below 5 ps with consistent latency is not a severe issue; it suggests stable timing behavior. In post- deployment troubleshooting, engineers should inspect switch counters, RoCE congestion statistics, ECN/PFC behavior, retransmissions, packet drops, and NCCL logs to determine whether the fabric is causing the observed latency spikes.

NEW QUESTION # 20
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. Offloading infrastructure services such as networking, storage, and security from the host CPU
B. Increasing GPU memory capacity through memory pooling
C. Executing CUDA kernels on behalf of GPUs
D. Replacing the InfiniBand fabric with PCIe communication

Answer: A

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 # 21
......

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