NCP-AIO Workload Management Practice Question
In an NVIDIA-accelerated Kubernetes environment, why is it critical to configure a 'RuntimeClass' for GPU-enabled pods?
⚠ Common exam trap
Candidates often assume that requesting a GPU resource in the pod spec is sufficient, forgetting that the RuntimeClass is the mechanism that actually injects the necessary NVIDIA libraries.
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
✓
To ensure the container runtime correctly injects GPU drivers and libraries into the pod.
The RuntimeClass ensures that the container is started with the correct NVIDIA container runtime (e.g., nvidia-container-runtime). This runtime is responsible for performing the necessary hardware mapping, injecting the CUDA libraries, and setting the environment variables required for the container to actually utilize the GPU. Without this configuration, the pod might be scheduled successfully but will fail at runtime because it cannot communicate with the NVIDIA driver.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To allow the Kubernetes scheduler to place pods on nodes based on GPU availability.
Why it's wrong here
Resource placement is handled by the device plugin and the scheduler, not the RuntimeClass. The RuntimeClass is strictly about the container runtime configuration (e.g., runc vs. nvidia-container-runtime) and is involved only after the scheduler has successfully decided which node the pod will run on.
- ✗
To define the specific NVIDIA driver version required by the application.
Why it's wrong here
Drivers are typically installed on the host node, not defined by the pod runtime. The runtime ensures the container uses the installed host driver, but it does not dictate or enforce specific driver versions at the pod level. Version management is handled at the node image or operator level.
- ✓
To ensure the container runtime correctly injects GPU drivers and libraries into the pod.
Why this is correct
The RuntimeClass enables the node to switch to the NVIDIA-specific container runtime when needed. This runtime is responsible for the crucial task of injecting the required driver and library files, enabling the container to recognize and interact with the GPU hardware without requiring manual configuration in every single pod.
- ✗
To increase the priority of GPU workloads over standard CPU-only workloads.
Why it's wrong here
Priority and preemption are handled by PriorityClasses, not RuntimeClasses. A RuntimeClass deals with the technical interface between the container and the host system, whereas a PriorityClass controls the scheduling order, which are distinct concerns in the Kubernetes workload management lifecycle.
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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.