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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
Certificate Validity Period:	2 years
Exam Format:	Scenario-based items, Multiple-choice questions
Exam Price:	$400 USD
Related Certifications:	NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Networking (NCP-AIN)
Exam Duration:	120 minutes
Passing Score:	Pass/Fail only, no numerical score
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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
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	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	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	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 (Q116-Q121):

NEW QUESTION # 116
A researcher wants to allow a container to use only two specific GPUs out of four available ones (GPU 1 and GPU 2) on the host. The system is already set up for NVIDIA Docker with the container toolkit. Which Docker command will correctly assign only these GPUs to the container?

A. docker run --rm --gpus '"device=1,2"' nvidia/cuda nvidia-smi
B. docker run --gpus '"device=0,3"' nvidia/cuda nvidia-smi --query-gpu=uuid --format=csv
C. docker run --gpus '"device=0,1"' nvidia/cuda nvidia-smi --query-gpu=uuid --format=csv
D. docker run --rm --gpus 2 nvidia/cuda nvidia-smi

Answer: A

Explanation:
The Docker --gpus option can restrict a container to specific GPU IDs. Specifying device=1,2 exposes only GPU 1 and GPU 2 to the container, and running nvidia-smi inside the CUDA container verifies that only those GPUs are visible.

NEW QUESTION # 117
As the infrastructure lead for an NVIDIA AI Factory deployment, you have just uploaded the latest supported firmware packages to your DGX system. It is now critical to ensure all hardware components run the new firmware and the DGX returns to full operational capability. Which sequence best guarantees that all relevant components are correctly running updated firmware according to NVIDIA's documentation and recommended operational steps?

A. Initiate a cold power cycle on all node trays to activate firmware, follow with a DGX reboot procedure, and use the management interface to finish activating CPLD firmware on the host.
B. Perform a software-driven restart on the operating system of every compute node, then use advanced tools to check firmware status and reissue update commands if any firmware appears inactive afterward.
C. Execute a single operating system reboot on the DGX after the update process, then reset the software stack and verify status using diagnostic commands on each node.
D. Initiate the required cold reset or power cycle to activate updated firmware, reset the BMC using the recommended command, and perform an AC power cycle when required for EROT and CPLD firmware activation.

Answer: D

Explanation:
The correct sequence is to perform the required reset or power-cycle actions for the components being updated. NVIDIA DGX H100/H200 firmware guidance states that activation depends on component type:
BMC firmware requires a BMC reset using sudo ipmitool mc reset cold; PCIe switch, PCIe retimer, BIOS, and HGX GPU tray components require a cold system reset using sudo ipmitool chassis power cycle; and EROT or CPLD components require an AC power cycle by removing and restoring system power. NVIDIA also notes that an AC power cycle activates firmware for all updated components. A normal operating system reboot is not sufficient for every firmware class, and relying only on software restart can leave some components running old firmware. In large NVIDIA AI Factory deployments, firmware activation must be treated as part of server bring-up, not just package upload. After the required reset sequence, administrators should verify installed and running firmware versions, confirm NVSM health, and only then return the DGX system to workload scheduling.

NEW QUESTION # 118
What is the purpose of using NCCL in verifying east/west fabric in an NVIDIA AI Factory?
(Choose two.)

A. To measure the storage network performance.
B. To measure bandwidth between GPUs.
C. To measure the power consumption of GPUs.
D. To measure the latency between GPUs.

Answer: B,D

Explanation:
NCCL is used to validate GPU-to-GPU communication across the east/west fabric by testing collective communication performance. Measuring bandwidth and latency between GPUs confirms that the compute fabric is performing correctly for distributed AI workloads.

NEW QUESTION # 119
Which of the following tests should be used to check for the lowest possible latency between two nodes in a fabric?

A. ib_read_lat
B. ib_read_bw
C. iperf
D. ib_write_lat

Answer: D

Explanation:
ib_write_lat is the InfiniBand perftest utility used to measure RDMA write latency between two nodes. It is commonly used to validate the lowest practical fabric latency between hosts.

NEW QUESTION # 120
Why is it important to provide a large and high-performance local cache (using SSDs configured as RAID-0) for deep learning workloads on DGX systems?

A. Using local SSD cache in RAID-0 enables direct GPU access to files without host CPU involvement, further boosting performance.
B. Local SSD cache allows users to increase the number of NFS threads on the server without impacting storage reliability.
C. A local SSD cache in RAID-0 ensures that most training data is read only once from the network, significantly reducing NFS traffic.
D. Local SSD cache in RAID-0 is necessary to provide redundancy in case one of the drives fails during long training runs.

Answer: C

Explanation:
Deep learning training involves iterating over a dataset many times (epochs). If a 32-node cluster pulls the same dataset from a central NFS storage server for every epoch, the network and storage fabric quickly become a bottleneck due to "Incast" traffic. By using the high-speed NVMe drives internal to a DGX system (configured inRAID-0for maximum performance, not redundancy), the system can implement a local cache.
During the first epoch, data is pulled from the remote storage and simultaneously written to the local SSDs.
For all subsequent epochs, the training framework reads the data directly from the local RAID-0 array. This significantly reduces NFS trafficand network congestion, allowing the training to proceed at the full speed of the local NVMe storage ($25\text{ GB/s}+$ on modern DGX systems). Option C is incorrect because RAID-0 provides no redundancy; if a drive fails, the cache is lost, but since it is just a cache, the data still exists on the primary storage. Option B refers to GPUDirect Storage, which is a separate technology from local RAID-0 caching.

NEW QUESTION # 121
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