NCP-AIO Installation and Deployment Practice Question
An administrator observes that despite the GPU Operator being installed, the pods cannot access the GPU. What is the most likely cause if the NVIDIA container runtime is properly configured?
⚠ Common exam trap
Candidates often assume the issue is a Kubernetes misconfiguration or a pod error. They fail to check the underlying host kernel, where driver loading issues are the most frequent root cause.
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
✓
The host NVIDIA driver is not loaded.
If the runtime is configured, the most common remaining failure is the lack of the correct NVIDIA drivers on the host node. If the kernel modules are not loaded or the driver version is incompatible with the installed GPU, the runtime will be unable to successfully inject the device nodes. This is a common installation oversight where the operator might be deployed but the driver installation task failed or was skipped.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Kubernetes API server is down.
Why it's wrong here
If the API server were down, the entire cluster would be non-functional, and no pods would be starting. The problem described is specific to GPU access, not general cluster orchestration failures, so blaming the API server ignores the scope of the issue related specifically to the hardware acceleration.
- ✓
The host NVIDIA driver is not loaded.
Why this is correct
Even with the correct runtime, the container requires the underlying host driver to communicate with the hardware. If the driver is not installed or the kernel module is not loaded, the runtime will fail to map the GPU device, leading to a situation where the GPU is inaccessible.
- ✗
The pod has too little memory requested.
Why it's wrong here
Memory requests affect scheduling, not the ability of a successfully scheduled pod to access hardware. If the pod is running but cannot see the GPU, the issue is at the kernel or runtime level, not the resource request level. Memory settings do not influence hardware device visibility.
- ✗
The container image is missing the CUDA library.
Why it's wrong here
While a missing library would cause an application-level 'library not found' error, it would not manifest as an inability to access the GPU at the hardware level. The hardware would still be visible, just not usable by the application. This is a common troubleshooting misidentification that misinterprets software dependencies.
About these practice questions
This NCP-AIO question is part of Courseiva's 309-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.