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NVIDIA NCP-AIO Exam Questions

NVIDIA NCP-AIO Exam Questions Answers

NVIDIA AI Operations

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NVIDIA NCP-AIO Sample Questions – Free Practice Test & Real Exam Prep

Question #1

You are managing a Slurm cluster with multiple GPU nodes, each equipped with differenttypes of GPUs. Some jobs are being allocated GPUs that should be reserved for otherpurposes, such as display rendering.How would you ensure that only the intended GPUs are allocated to jobs?

  • A. Verify that the GPUs are correctly listed in both gres.conf and slurm.conf, and ensure that unconfigured GPUs are excluded.
  • B. Use nvidia-smi to manually assign GPUs to each job before submission. 
  • C. Reinstall the NVIDIA drivers to ensure proper GPU detection by Slurm. 
  • D. Increase the number of GPUs requested in the job script to avoid using unconfigured GPUs. 
Answer: A
Explanation:
Slurm relies on gres.conf and slurm.conf to know which GPUs are available for job
allocation. By explicitly listing only the intended GPUs in these configuration files and
excluding others such as display GPUs, you ensure Slurm never allocates the wrong ones.
The other options are either manual workarounds or completely unrelated to the actual
configuration problem.
Question #2

Your organization is deploying an AI workload that requires high-throughput access toshared storage across multiple servers. The workload involves both training and inferencetasks that need fast read and write speeds.Which storage architecture would best support this AI workload?

  • A. Use local storage on each server to minimize network traffic between nodes. 
  • B. Prioritize write performance over read performance since training tasks dominate AI workflows.
  • C. A high-performance shared storage system that supports both high read and write IO performance.
  • D. Use SSD-based shared storage systems to save costs while scaling up storage capacity.
Answer: C
Explanation:
AI workloads involving both training and inference across multiple servers require shared
storage so all nodes can access the same datasets simultaneously. Training demands high
read throughput for loading data and high write performance for saving checkpoints, while
inference needs fast reads for model loading. Local storage creates data silos and doesn't
scale across nodes, and optimizing for only read or write performance misses the balanced
requirements of this workload.
Question #3

An administrator is troubleshooting issues with an NVIDIA Unified Fabric ManagerEnterprise (UFM) installation and notices that the UFM server is unable to communicatewith InfiniBand switches.What step should be taken to address the issue?

  • A. Reboot the UFM server to refresh network connections. 
  • B. Install additional GPUs in the UFM server to boost connectivity. 
  • C. Disable the firewall on the UFM server to allow communication. 
  • D. Verify the subnet manager configuration on the InfiniBand switches. 
Answer: D
Explanation:
The subnet manager is responsible for managing the InfiniBand fabric, including routing and
addressing. If UFM cannot communicate with the InfiniBand switches, a misconfigured or
inactive subnet manager is the most likely root cause. Rebooting the server or disabling the
firewall are superficial fixes that don't address the underlying fabric communication issue,
and adding GPUs has nothing to do with network connectivity.
Question #4

You are tasked with deploying a deep learning framework container from NVIDIA NGC ona stand-alone GPU-enabled server.What must you complete before pulling the container? (Choose two.)

  • A. Install Docker and the NVIDIA Container Toolkit on the server. 
  • B. Set up a Kubernetes cluster to manage the container. 
  • C. Install TensorFlow or PyTorch manually on the server before pulling the container. 
  • D. Generate an NGC API key and log in to the NGC container registry using docker login. 
Answer: A,D
Explanation:
Before pulling a container from NGC, you need Docker and the NVIDIA Container Toolkit
installed so the server can run GPU-accelerated containers, and you need an NGC API key
to authenticate with the private NGC registry via docker login nvcr.io. Kubernetes is not
required for a standalone server, and frameworks like TensorFlow or PyTorch come preinstalled inside the NGC container itself — there's no need to install them manually on the
host.
Question #5

A system administrator is looking to set up virtual machines in an HGX environment withNVIDIA Fabric Manager.What three (3) tasks will Fabric Manager accomplish? (Choose three.)

  • A. Configures routing among NVSwitch ports. 
  • B. Installs GPU operator 
  • C. Coordinates with the NVSwitch driver to train NVSwitch to NVSwitch NVLink interconnects.
  • D. Coordinates with the GPU driver to initialize and train NVSwitch to GPU NVLink interconnects.
  • E. Installs vGPU driver as part of the Fabric Manager Package. 
Answer: A,C,D
Explanation:
Fabric Manager's core responsibilities are all about managing the NVLink and NVSwitch
fabric. It configures routing among NVSwitch ports, coordinates with the NVSwitch driver to
train NVSwitch-to-NVSwitch NVLink interconnects, and coordinates with the GPU driver to
initialize and train NVSwitch-to-GPU NVLink interconnects. Installing the GPU operator or
vGPU driver are not Fabric Manager functions. Those are handled separately through their
own installation processes.
Question #6

You are setting up a Kubernetes cluster on NVIDIA DGX systems using BCM, and youneed to initialize the control-plane nodes.What is the most important step to take before initializing these nodes?

  • A. Set up a load balancer before initializing any control-plane node. 
  • B. Disable swap on all control-plane nodes before initializing them. 
  • C. Ensure that Docker is installed and running on all control-plane nodes. 
  • D. Configure each control-plane node with its own external IP address. 
Answer: B
Explanation:
Kubernetes requires swap to be disabled on all nodes before initialization. If swap is enabled,
the kubelet will fail to start because Kubernetes relies on precise memory management and
swap interferes with that. This is a hard prerequisite that must be done before running
kubeadm init on any control-plane node, regardless of the other configuration steps.
Question #7

A system administrator needs to configure and manage multiple installations of NVIDIAhardware ranging from single DGX BasePOD to SuperPOD.Which software stack should be used?

  • A. NetQ 
  • B. Fleet Command 
  • C. Magnum IO 
  • D. Base Command Manager 
Answer: D
Explanation:
BCM is purpose-built for configuring and managing NVIDIA DGX infrastructure at any scale,
from a single DGX BasePOD all the way up to SuperPOD deployments. Fleet Command is
for edge AI deployments, Magnum IO is a storage and networking performance framework,
and NetQ is a network monitoring tool — none of these are designed for on-premises DGX
cluster management.
Question #8

A Fleet Command system administrator wants to create an organization user that will havethe following rights:For locations - read onlyFor Applications - read/write/adminFor Deployments - read/write/adminFor Dashboards - read onlyWhat role should the system administrator assign to this user?

  • A. Fleet Command Operator 
  • B. Fleet Command Admin 
  • C. Fleet Command Supporter 
  • D. Fleet Command Viewer 
Answer: A
Explanation:
The Fleet Command Operator role matches this permission profile exactly — read-only
access to locations and dashboards, while having full read/write/admin rights over
applications and deployments. The Viewer role is too restrictive, the Admin role grants full
access to everything including locations, and the Supporter role is oriented toward support
functions rather than application and deployment management.
Question #9

A new researcher needs access to GPU resources but should not have permission tomodify cluster settings or manage other users.What role should you assign them in Run:ai?

  • A. L1 Researcher 
  • B. Department Administrator 
  • C. Application Administrator 
  • D. Research Manager 
Answer: A
Explanation:
The L1 Researcher role in Run:ai is designed exactly for this use case — it grants access to
submit and manage their own GPU workloads without any ability to modify cluster settings or
manage other users. Department Administrator and Application Administrator both carry
broader administrative privileges, and Research Manager has team-level oversight
capabilities beyond what a new researcher should need.
Question #10

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?

  • A. nvsm show logs --save 
  • B. nvsm get logs 
  • C. nvsm dump health 
  • D. nvsm health --dump-log 
Answer: C
Explanation:
nvsm dump health generates and saves a comprehensive system health report log on DGX
systems. It is specifically designed for diagnostics, capturing detailed health information
across GPUs, storage, thermals, and other subsystems into a saved log file, making it the
right tool when troubleshooting performance issues on a DGX H100.
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