Most Recent NVIDIA NCP-AIO Exam Dumps

Prepare for the NVIDIA AI Operations exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the NVIDIA NCP-AIO exam and achieve success.

The questions for NCP-AIO were last updated on Apr 21, 2026.

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Question No. 1

A DGX H100 system in a cluster is showing performance issues when running jobs.

Which command should be run to generate system logs related to the health report?

Anvsm show logs --save

Bnvsm get logs

Cnvsm dump health

Dnvsm health --dump-log

Show Answer

Correct Answer: C

Comprehensive and Detailed Explanation From Exact Extract:

For troubleshooting and performance optimization on NVIDIA DGX systems such as DGX H100, the NVIDIA System Management (nvsm) tool is used to gather system health and diagnostic data. The command nvsm dump health is the correct command to generate and export detailed system logs related to the health report of the DGX system.

nvsm show logs --save is not a recognized command format.

nvsm get logs retrieves logs but does not specifically dump the health report logs.

nvsm health --dump-log is not a standard documented nvsm command.

Therefore, nvsm dump health is the valid and documented command used to generate system logs focused on health reporting, useful for diagnosing performance issues in DGX H100 systems.

This usage aligns with NVIDIA's system management tools guidance for DGX platforms as described in NVIDIA AI Operations documentation for troubleshooting and performance optimization.

Question No. 2

An instance of NVIDIA Fabric Manager service is running on an HGX system with KVM. A System Administrator is troubleshooting NVLink partitioning.

By default, what is the GPU polling subsystem set to?

AEvery 1 second

BEvery 30 seconds

CEvery 60 seconds

DEvery 10 seconds

Show Answer

Correct Answer: B

Comprehensive and Detailed Explanation From Exact Extract:

In NVIDIA AI infrastructure, the NVIDIA Fabric Manager service is responsible for managing GPU fabric features such as NVLink partitioning on HGX systems. This service periodically polls the GPUs to monitor and manage NVLink states. By default, the GPU polling subsystem is set to every 30 seconds to balance timely updates with system resource usage.

This polling interval allows the Fabric Manager to efficiently detect and respond to changes or issues in the NVLink fabric without excessive overhead or latency. It is a standard default setting unless specifically configured otherwise by system administrators.

This default behavior aligns with NVIDIA's system management guidelines for HGX platforms and is referenced in NVIDIA AI Operations materials concerning fabric management and troubleshooting of NVLink partitions.

Question No. 3

You are managing a deep learning workload on a Slurm cluster with multiple GPU nodes, but you notice that jobs requesting multiple GPUs are waiting for long periods even though there are available resources on some nodes.

How would you optimize job scheduling for multi-GPU workloads?

AReduce memory allocation per job so more jobs can run concurrently, freeing up resources faster for multi-GPU workloads.

BEnsure that job scripts use --gres=gpu:<number> and configure Slurm's backfill scheduler to prioritize multi-GPU jobs efficiently.

CSet up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts between them.

DIncrease time limits for smaller jobs so they don't interfere with multi-GPU job scheduling.

Show Answer

Correct Answer: B

Comprehensive and Detailed Explanation From Exact Extract:

To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in job scripts using --gres=gpu:<number> and enable/configure Slurm's backfill scheduler. Backfill allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs, improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration ensures efficient packing and priority handling of GPU resources.

Question No. 4

You are managing a Kubernetes cluster running AI training jobs using TensorFlow. The jobs require access to multiple GPUs across different nodes, but inter-node communication seems slow, impacting performance.

What is a potential networking configuration you would implement to optimize inter-node communication for distributed training?

AIncrease the number of replicas for each job to reduce the load on individual nodes.

BUse standard Ethernet networking with jumbo frames enabled to reduce packet overhead during communication.

CConfigure a dedicated storage network to handle data transfer between nodes during training.

DUse InfiniBand networking between nodes to reduce latency and increase throughput for distributed training jobs.

Show Answer

Correct Answer: D

Comprehensive and Detailed Explanation From Exact Extract:

For distributed AI training jobs that require fast inter-node communication, such as those using TensorFlow across multiple GPUs and nodes, InfiniBand networking is the preferred solution. InfiniBand provides ultra-low latency and high bandwidth, reducing communication delays significantly and increasing overall training throughput. While jumbo frames on Ethernet can help, they do not match the performance of InfiniBand. Dedicated storage networks or increasing replicas do not directly address inter-node communication latency.

Question No. 5

You have noticed that users can access all GPUs on a node even when they request only one GPU in their job script using --gres=gpu:1. This is causing resource contention and inefficient GPU usage.

What configuration change would you make to restrict users' access to only their allocated GPUs?

AIncrease the memory allocation per job to limit access to other resources on the node.

BEnable cgroup enforcement in cgroup.conf by setting ConstrainDevices=yes.

CSet a higher priority for Jobs requesting fewer GPUs, so they finish faster and free up resources sooner.

DModify the job script to include additional resource requests for CPU cores alongside GPUs.

Show Answer

Correct Answer: B

Comprehensive and Detailed Explanation From Exact Extract:

To restrict users' access strictly to the GPUs allocated to their jobs, Slurm uses cgroups (control groups) for resource isolation. Enabling device cgroup enforcement by setting ConstrainDevices=yes in cgroup.conf enforces device access restrictions, ensuring jobs cannot access GPUs beyond those assigned.

Increasing memory allocation or setting job priorities does not restrict device access.

Modifying job scripts to request additional CPU cores does not limit GPU access.

Hence, enabling cgroup enforcement with ConstrainDevices=yes is the correct method to prevent users from accessing unallocated GPUs.

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