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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
Available Languages:English
Exam Format:Scenario-based items, Multiple-choice questions
Exam Price:$400 USD
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)
Certificate Validity Period:2 years
Exam Duration:120 minutes
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:

TopicDetails
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
  • 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 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
  • 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.

NVIDIA AI Infrastructure Sample Questions (Q150-Q155):

NEW QUESTION # 150
You have a deep learning application that requires a specific version of the CUDA toolkit inside the container. How should you best ensure that the correct CUDA version is available within the container, considering the NVIDIA Container Toolkit is installed on the host?

Answer: E

Explanation:
The recommended approach is to use a base image that already contains the desired CUDA version. NVIDIA provides pre-built images on NGC (NVIDIA GPU Cloud) that are specifically designed for deep learning and include the appropriate CUDA versions and other dependencies. Installing CUDA on the host and expecting it to be magically mapped (A) is not reliable. The NVIDIA Container Toolkit doesn't install CUDA on the fly (B). Manually copying libraries (D) is error-prone and doesn't handle dependencies well. While technically possible, using nvidia- container-cli to modify the image is more complex than using a base image.


NEW QUESTION # 151
You are tasked with installing the NGC CLI on a host that does not have direct internet access. You have downloaded the NGC CLI package to a local repository. Which of the following steps are required to successfully install and configure the NGC CLI in this offline environment?

Answer: A,B,C,D

Explanation:
In an offline environment, you need to install the package locally (A), configure the CLI to know where to find the package (B), manually install dependencies (C), and configure the API key (D). Option E is wrong because dependencies must be handled manually in the offline environment.


NEW QUESTION # 152
A system administrator needs to validate a GPU-based server and ensure that no errors occur under load. What command should be used?

Answer: D

Explanation:
nvsm stress-test is used to place the DGX/GPU-based server under load and validate that the system remains stable without reporting hardware or GPU errors during the test.


NEW QUESTION # 153
An AI cluster needs to transmit data at 200Gbps over a distance of 2km using single-mode fiben Considering cost and performance, which transceiver type is the most appropriate?

Answer: A

Explanation:
200GBASE-LR4 is the most appropriate. 'LR' designates Long Reach, typically up to 10km on single-mode fiber. SR4 is short reach (typically copper or very short fiber). ER4 is Extended Reach (up to 40km). CR4 is copper, for very short distances. DR4 is typically used for up to 500m to 2km distances depending on the specific implementation, so while possible LR4 is a better fit for guaranteed 2km.


NEW QUESTION # 154
For an NVIDIA Enterprise Al Factory with 256 GPUs, which storage solution characteristic is most critical to validate during scaling tests?

Answer: A

Explanation:
At 256 GPUs, the key storage scaling concern is whether every compute node can sustain the required throughput concurrently. Consistent per-node throughput of at least 8 GiB/s validates that the storage system can keep GPUs fed with training data at scale without becoming a bottleneck.


NEW QUESTION # 155
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