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NVIDIA NCP-AII Exam Overview:

Certification Vendor:	NVIDIA
Exam Name:	NVIDIA AI Infrastructure (NCP-AII) Certification Exam
Exam Number:	NCP-AII
Related Certifications:	NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO)
Exam Format:	Multiple-choice, Scenario-based questions
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Exam Price:	$400
Real Exam Qty:	70-75
Available Languages:	English
Recommended Training:	AI Infrastructure Professional Workshop AI Infrastructure & Operations Fundamentals (NVIDIA Training)
Exam Registration:	NVIDIA AI Infrastructure Certification Page Official NVIDIA Certification Portal
Sample Questions:	NVIDIA NCP-AII Sample Questions
Exam Way:	Online proctored exam (remote) or authorized test center depending on region
Pre Condition:	Recommended 2–3 years of experience working in data center environments with NVIDIA hardware solutions (GPU servers, networking, storage).
Official Syllabus URL:	https://www.nvidia.com/en-eu/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	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	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 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 (Q84-Q89):

NEW QUESTION # 84
A system administrator has upgraded the firmware of the DPU. What will be the state of the firmware after the upgrade?

A. The firmware is installed on the DPU.
B. The firmware is waiting on reboot to become active.
C. The firmware is copied to the DPU but not installed.
D. The firmware is deleted from the DPU.

Answer: B

Explanation:
After a DPU firmware upgrade, the new firmware is staged but does not become active until the DPU is rebooted or reset as required. The reboot activates the newly installed firmware image.

NEW QUESTION # 85
Refer to the exhibit. What is the maximum bandwidth supported by Direct Attach Copper (DAC) LinkX cables?

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

Answer: D

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 # 86
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
B. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.
C. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
D. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
E. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.

Answer: B,C,D,E

Explanation:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

NEW QUESTION # 87
You are deploying a new NVLink Switch based cluster. The GPUs are installed in different servers, but need to be configured to utilize NVLink interconnect. Which of the following should be performed during the installation phase to confirm correct configuration?

A. All the GPU's are in the same IP subnet
B. Verify that GPUDirect RDMA is enabled and functioning correctly.
C. Run NCCL tests to verify the GPU-to-GPU bandwidth and latency between servers.
D. Check that the 'nvidia-sm' command shows the correct NVLink topology.
E. Run standard TCP/IP network bandwidth tests to check inter-server communication.

Answer: B,C,D

Explanation:
NCCL tests are specifically designed to test GPU-to-GPU communication. Ensuring GPUDirect RDMA is functioning is essential for low-latency communication. 'nvidia-smi' should display the NVLink topology. TCP/IP tests do not test the NVLink connection. It does not matter if GPUs on different servers are in the same IP subnet as NVLink communication occur directly between the GPUs using RDMA mechanism. Subnetting affects traditional networking layer communication, but not low-level device communication.

NEW QUESTION # 88
A user encounters "permission denied" errors when running GPU-accelerated containers on a Secure Boot-enabled system. What resolves this?

A. Disable SELinux to relax unnecessary security policies.
B. Run Docker with sudofor elevated privileges.
C. Reinstall Docker without the NVIDIA runtime.
D. Enroll the MOK and sign NVIDIA kernel modules.

Answer: D

Explanation:
On Secure Boot-enabled systems, unsigned NVIDIA kernel modules may be blocked from loading, which prevents GPU access inside containers and can appear as permission-related failures. Enrolling the Machine Owner Key and signing the NVIDIA kernel modules allows the trusted modules to load correctly while Secure Boot remains enabled.

NEW QUESTION # 89
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