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NVIDIA NCP-AIO Exam With Confidence Using Practice Dumps

Exam Code:
NCP-AIO
Exam Name:
NVIDIA AI Operations
Vendor:
Questions:
66
Last Updated:
Jul 12, 2025
Exam Status:
Stable
NVIDIA NCP-AIO

NCP-AIO: NVIDIA-Certified Professional Exam 2025 Study Guide Pdf and Test Engine

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NVIDIA AI Operations Questions and Answers

Question 1

A system administrator is experiencing issues with Docker containers failing to start due to volume mounting problems. They suspect the issue is related to incorrect file permissions on shared volumes between the host and containers.

How should the administrator troubleshoot this issue?

Options:

A.

Use the docker logs command to review the logs for error messages related to volume mounting and permissions.

B.

Reinstall Docker to reset all configurations and resolve potential volume mounting issues.

C.

Disable all shared folders between the host and container to prevent volume mounting errors.

D.

Reduce the size of the mounted volumes to avoid permission conflicts during container startup.

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Question 2

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?

Options:

A.

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

B.

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

C.

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

D.

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

Question 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?

Options:

A.

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

B.

Ensure that job scripts use --gres=gpu: and configure Slurm’s backfill scheduler to prioritize multi-GPU jobs efficiently.

C.

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

D.

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