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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
Exam Format:	Scenario-based items, Multiple-choice questions
Exam Duration:	120 minutes
Related Certifications:	NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO)
Exam Price:	$400 USD
Certificate Validity Period:	2 years
Available Languages:	English
Passing Score:	Pass/Fail only, no numerical score
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	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 2	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3	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 4	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 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 (Q81-Q86):

NEW QUESTION # 81
A systems administrator needs to provide an AI workload environment for a developer. Which profile type should the Administrator choose for vGPU?

A. A-series
B. C-series
C. B-series
D. Q-series

Answer: B

Explanation:
C-series vGPU profiles are intended for compute workloads such as AI, deep learning, data science, and high-performance computing. They provide a vGPU profile type optimized for GPU- accelerated workload environments rather than graphics-focused use cases.

NEW QUESTION # 82
When configuring an out-of-core (OOC) HPL burn-in for a 40B matrix on 8x H100 nodes, which environment variable prevents GPU out-of-memory errors while reserving space for drivers?

A. export HPL_OOC_MAX_GPU_MEM=90
B. export HPL_OOC_SAFE_SIZE=4.0
C. export HPL_OOC_MODE=0
D. export HPL_OOC_NUM_STREAMS=8

Answer: B

Explanation:
HPL_OOC_SAFE_SIZE reserves a safety margin of GPU memory for drivers, runtime overhead, and other allocations during out-of-core HPL execution. Setting it prevents the OOC run from consuming all available GPU memory and helps avoid out-of-memory failures during large matrix burn-in tests.

NEW QUESTION # 83
You want to automate the NGC CLI installation process across multiple hosts in your infrastructure. What are the best practices to achieve this?

A. Use a Dockerfile to create a container image with the NGC CLI pre-installed and configured.
B. Manually install the NGC CLI on each host, as automation is not recommended for security reasons.
C. Distribute the '-/.ngc/config.json' file to all hosts.
D. Use a configuration management tool like Ansible or Chef to automate the installation and configuration of the NGC CLI on all hosts.
E. Create a custom script that downloads the NGC CLI package, installs it using 'pip' , and configures the API key.

Answer: A,D,E

Explanation:
Automation is highly recommended. Configuration management tools (A), custom scripts (B), and containerization (D) are all viable options for automating the NGC CLI installation process. Manually installing on each host is inefficient and error-prone. Distributing the config.json (E) could be a security risk.

NEW QUESTION # 84
Consider the following scenario: You have a BlueField-2 DPU installed in a server. You are trying to establish RDMA (Remote Direct Memory Access) communication between the DPU and another server. However, the RDMA connection fails. Which of the following is the most crucial factor to verify in this scenario?

A. That the appropriate RDMA kernel modules (e.g., , 'ib_uverbs) are loaded on both the DPUand the remote server.
B. That the clocks on both servers are synchronized using NTP (Network Time Protocol).
C. That the TCP window size is properly tuned for high-bandwidth communication.
D. That the correct MTU (Maximum Transmission Unit) is configured on the network interfaces involved in the RDMA connection.
E. That the power supply to the DPU is providing sufficient wattage.

Answer: A

Explanation:
For RDMA to function correctly, the necessary kernel modules must be loaded on both ends of the connection. These modules provide the core functionality for RDMA communication. While MTU and other network settings are important, the absence of the RDMA modules will prevent the connection from being established in the first place. Clock synchronization and TCP window size are less directly related to the initial RDMA connection failure. Power supply issues would likely manifest in other ways.

NEW QUESTION # 85
You have installed an NVIDIA ConnectX-7 network adapter in an A1 server and configured RDMA over Converged Ethernet (RoCE). During validation, you observe very high latency between two servers communicating over RoCE. Which of the following are potential causes? (Choose two)

A. The network switch does not support RoCE.
B. Insufficient memory on the network adapter.
C. Incorrect MTU size configuration on the network interfaces.
D. The network cables are damaged.
E. The GPU driver is outdated.

Answer: A,C

Explanation:
RoCE requires specific switch support and a properly configured MTU. Damaged cables could cause packet loss, but usually not consistently high latency. GPU drivers are irrelevant. Network adapter memory is unlikely to cause high latency unless extremely undersized, a less likely scenario than incorrect configuration or lack of RoCE support.

NEW QUESTION # 86
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