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| Vendor: | NVIDIA |
|---|---|
| Exam Code: | NCP-AIO |
| Exam Name: | AI Operations |
| Exam Questions: | 66 |
| Last Updated: | January 9, 2026 |
| Related Certifications: | NVIDIA-Certified Professional |
| Exam Tags: | Professional NVIDIA System Administrators and AI Infrastructure Engineers |
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A system administrator needs to scale a Kubernetes Job to 4 replicas.
What command should be used?
Comprehensive and Detailed Explanation From Exact Extract:
The correct command to scale a Kubernetes Job to a specific number of replicas is kubectl scale job --replicas=4. This explicitly sets the number of desired pod instances for the Job resource. The other commands are either invalid (stretch), apply to Deployments rather than Jobs (autoscale deployment), or use incorrect syntax (-r).
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?
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.
What should an administrator check if GPU-to-GPU communication is slow in a distributed system using Magnum IO?
Comprehensive and Detailed Explanation From Exact Extract:
Slow GPU-to-GPU communication in distributed systems often relates to the configuration of communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM. Ensuring these libraries are properly configured and optimized is critical for efficient GPU communication. Limiting GPUs or increasing RAM does not directly improve communication speed, and disabling InfiniBand would degrade performance.
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?
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.
You are tasked with deploying a deep learning framework container from NVIDIA NGC on a stand-alone GPU-enabled server.
What must you complete before pulling the container? (Choose two.)
Comprehensive and Detailed Explanation From Exact Extract:
Before pulling and running an NVIDIA NGC container on a stand-alone server, you must:
Install Docker and the NVIDIA Container Toolkit to enable container runtime with GPU support.
Generate an NGC API key and authenticate with the NGC container registry using docker login to pull private or public containers.
Setting up Kubernetes or manually installing deep learning frameworks is unnecessary when using containers as they include the required frameworks.
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