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 NCP-AIN Dumps with Practice Exam Questions Answers

Questions: 70 Questions and Answers With Step-by-Step Explanation

Last Update: Mar 4, 2026

NCP-AIN Question Includes: Single Choice Questions: 63, Multiple Choice Questions: 7,

NCP-AIN Questions and Answers

Question # 1

When utilizing the ib_write_bw tool for performance testing, what does the -S flag define?

A.

The burst size

B.

The number of QP's

C.

The maximum rate of sent packages

D.

Which service level to use

Question # 2

You are automating the deployment of a Spectrum-X network using Ansible. You need to ensure that the playbooks can handle different switch models and configurations efficiently.

Which feature of the NVIDIA NVUE Collection helps simplify the automation by providing pre-built roles for common network configurations?

A.

Collection libraries

B.

Collection modules

C.

Collection roles

D.

Collection plugins

Question # 3

When upgrading Cumulus Linux to a new version, which configuration files should be migrated from the old installation?

Pick the 2 correct responses below.​

A.

All files in /etc/cumulus/acl

B.

All files in /etc/network

C.

All files in /etc

D.

All files in /etc/mix​

Question # 4

You are troubleshooting an InfiniBand network issue and need to check the status of the InfiniBand interfaces. Which command should you use to display the state, physical state, and link layer of InfiniBand interfaces?

A.

ibv_devices -c mlx5_0

B.

cat /proc/net/ib/device

C.

sudo ibnodes -C mlx5_0

D.

ibstat -d mlx5_X

Question # 5

What is the purpose of configuring NVUE to ignore Linux files?​

A.

Enable pushing of configuration through Ansible template files.

B.

Enable the persistent manipulation of specific settings using both NVUE and flat-file approaches.

C.

Reduce NVUE memory utilization to optimize performance.

D.

Improve Cumulus security by reducing the attack surface.​

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NVIDIA NCP-AIN Practice Exam FAQs

1. What is the NVIDIA NCP-AIN Exam?


The NVIDIA NCP-AIN Exam, officially known as the NVIDIA Certified Professional – AI Networking, validates your ability to deploy, configure, and optimize environments that leverage NVIDIA’s advanced networking technologies for AI workloads.

2. Who should take the NCP-AIN Exam?


The NCP-AIN exam is ideal for network engineers, AI infrastructure specialists, and IT professionals with 2–3 years of operational experience in data centers or AI networking environments.

3. What topics are covered in the NCP-AIN Exam?


The exam syllabus includes:

  • Architecture (20%) – AI factory vs. AI data center architectures, Ethernet vs. InfiniBand, storage types.

  • Spectrum-X Configuration, Optimization, Security, and Troubleshooting (40%) – Switch configuration, monitoring, and security.

  • Networking for AI Workloads (20%) – High-performance networking for AI training and inference.

  • Operational Best Practices (20%) – Deployment, monitoring, and troubleshooting.

4. How many questions are on the NCP-AIN Exam?


The NVIDIA NCP-AIN exam consists of 70-75 multiple-choice questions, with a time limit of 120 minutes.

5. How much does the NCP-AIN Exam cost?


The registration fee for the exam is $400 USD.

6. What prerequisites are required for the NCP-AIN Exam?


Candidates should have 2–3 years of operational experience working in data centers or AI networking environments before attempting the exam.

7. What is the difference between NVIDIA NCP-AIN and NCP-AIO?


The NVIDIA NCP-AIN and NCP-AIO certifications focus on different aspects of AI infrastructure.

  • The NCP-AIN (AI Networking) is centered on networking for AI workloads, covering topics like Spectrum-X switch configuration, InfiniBand vs. Ethernet, security, and troubleshooting. It’s designed for professionals building and optimizing AI data center networks.

  • The NCP-AIO (AI Operations) focuses on the operational side of AI infrastructure, including monitoring, troubleshooting, and workload management with tools like Base Command Manager, Kubernetes GPU Operator, Slurm, DCGM, and MIG. It’s aimed at professionals managing the day-to-day operations of AI systems.

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