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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
Real Exam Qty:70-75
Exam Price:$400
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)
Certificate Validity Period:2 years
Available Languages:English
Exam Duration:120 minutes
Exam Format:Scenario-based questions, Multiple-choice
Recommended Training:AI Infrastructure Professional Workshop
AI Infrastructure & Operations Fundamentals (NVIDIA Training)
Exam Registration:Official NVIDIA Certification Portal
NVIDIA AI Infrastructure Certification Page
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:

TopicDetails
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
  • 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 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
  • 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 (Q97-Q102):

NEW QUESTION # 97
After ClusterKit reports "GPU-Host latency exceeds threshold", which NVIDIA diagnostic tool should be used to isolate hardware faults?

Answer: B

Explanation:
DCGM Diagnostics with dcgmi diag -r 2 is the NVIDIA hardware diagnostic tool used to isolate GPU-related hardware faults after performance or latency anomalies are detected. It performs deeper GPU health validation beyond topology inspection or workload reruns.


NEW QUESTION # 98
A large A1 model is training using a dataset stored on a network-attached storage (NAS) device. The data transfer speeds are significantly lower than expected. After initial troubleshooting, you discover that the MTU (Maximum Transmission Unit) size on the network interfaces of the training server and the NAS device are mismatched. The server is configured with an MTIJ of 1500, while the NAS device is configured with an MTU of 9000 (Jumbo Frames). What is the MOST likely consequence of this MTU mismatch, and what action should you take?

Answer: A

Explanation:
An MTU mismatch (option A) will cause fragmentation, where larger packets are broken down into smaller packets before being transmitted, adding overhead and reducing performance. The solution is to configure both devices to use the same MTU size. Choosing 1500 ensures compatibility, while 9000 requires the entire network path to support jumbo frames.


NEW QUESTION # 99
You are setting up a multi-node A1 cluster with NVIDIA GPUs and InfiniBand for inter-node communication. You need to ensure the InfiniBand network is functioning optimally for GPU-accelerated workloads. What steps would you take to validate the InfiniBand installation and performance?

Answer: B

Explanation:
Sibstat' verifies interface status. 'ibping' and 'ibperf are InfiniBand-specific tools for latency and bandwidth testing. NCCL (NVIDIA Collective Communications Library) is critical for distributed training, and provides valuable diagnostic information. The other options are either incomplete or rely on tools not specific to InfiniBand.


NEW QUESTION # 100
You are trying to install the NVIDIA Container Toolkit on a Linux distribution that is not officially supported in the NVIDIA documentation.
The standard installation instructions using 'apt' or "yum' fail. What is the most appropriate approach to proceed with the installation?

Answer: D

Explanation:
The most practical approach is to try adapting the installation instructions from a similar, supported distribution (B). This involves carefully examining the package dependencies and potential compatibility issues. Manually installing drivers and CUDA (A) is complex and doesn't provide the containerization benefits. Compiling from source (C) might be possible but requires significant expertise and is not the recommended path. Running the application in a container (D) is a workaround, not a solution to installing the toolkit on the host. Requesting a custom package (E) is unlikely to be successful in a timely manner. The goal is to install the NVIDIA Container Toolkit itself, and not only run A1 applications.


NEW QUESTION # 101
An A1 server exhibits frequent kernel panics under heavy GPU load. 'dmesg' reveals the following error: 'NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.' Which of the following is the least likely cause of this issue?

Answer: A

Explanation:
The error message GPU has fallen off the bus strongly suggests a hardware-related issue with the GPU's connection to the motherboard or its power supply. Insufficient power, a loose riser cable, driver bugs and overclocking can all lead to this. A faulty CPU, while capable of causing system instability, is less directly related to the GPIJ falling off the bus and therefore the least likely cause in this specific scenario.


NEW QUESTION # 102
......

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