NCP-AIO Workload Management Practice Question
A cluster administrator notices that GPU utilization is low despite high queue volume. After analyzing the logs, they identify that many pods are failing because they cannot access the necessary CUDA libraries. What is the most likely cause, and which component should be verified?
⚠ Common exam trap
Candidates often blame the GPU driver version first. While important, the runtime mapping is the most common configuration error that prevents the container from 'seeing' the host's GPU capabilities.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check the NVIDIA Container Runtime configuration for proper runtime class mapping.
The most likely cause is a misconfiguration of the NVIDIA Container Runtime, which is responsible for injecting the necessary NVIDIA libraries into the container namespace. If the container runtime is not properly configured, the container cannot interact with the GPU, even if the scheduler has successfully placed it. Verifying the container runtime configuration is essential to ensure that GPU-enabled containers can access host-level drivers and CUDA stacks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Verify the NVIDIA Device Plugin status, as it manages library injection.
Why it's wrong here
The device plugin manages the exposure of hardware resources but is not responsible for injecting libraries into the container. The library injection is handled by the container runtime, which needs to be configured correctly to bind-mount the necessary driver files into the container at start-up.
- ✓
Check the NVIDIA Container Runtime configuration for proper runtime class mapping.
Why this is correct
The NVIDIA Container Runtime must be configured to inject the appropriate drivers and libraries into the container. If this mapping is missing, applications will fail to load CUDA libraries, leading to runtime errors. Checking the container runtime ensures that the environment is set up for correct GPU access.
- ✗
Review Kubernetes Resource Quotas, as they restrict the total amount of GPU memory.
Why it's wrong here
Resource quotas manage the total capacity available, but they do not cause library-loading errors. If a quota were exceeded, the pod would simply be blocked by the scheduler or rejected by the admission controller. Library access issues are strictly related to the container environment configuration.
- ✗
Increase the GPU memory limit to ensure enough memory for loading heavy CUDA libraries.
Why it's wrong here
CUDA libraries reside on the disk and are loaded into memory, but failing to access them is a linkage or path issue, not a lack of capacity. Increasing GPU memory will not solve an issue where the container cannot find the files it needs to interface with the hardware.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-AIO practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-AIO exam.