NCP-AIO Workload Management Practice Question
When debugging a workload that consistently crashes with 'Out of Memory' (OOM) errors despite sufficient GPU VRAM, what is the most likely cause related to workload management?
⚠ Common exam trap
Candidates mistakenly assume that OOM errors on GPU nodes are always caused by insufficient VRAM, completely ignoring container system memory limits during data preprocessing.
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 container memory limit is lower than the data processing requirements.
OOM errors can occur due to host-side system memory exhaustion if the workload manages large datasets in system RAM before loading them into the GPU. If the container memory limit is set too low for the data processing pipeline, the entire container will be terminated. This highlights the need to correctly balance both GPU VRAM and system memory limits in the container specification.
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 CUDA driver version is incompatible with the installed GPU.
Why it's wrong here
Driver incompatibility typically results in runtime initialization failures or errors when calling CUDA APIs, rather than OOM errors during workload execution. OOM errors specifically relate to resource exhaustion, and while driver issues might exacerbate performance, they are not the primary cause of an OOM error during normal operations.
- ✓
The container memory limit is lower than the data processing requirements.
Why this is correct
Workloads often require substantial system memory for preprocessing before moving data to the GPU. If the container memory limit is exceeded, the orchestrator will terminate the pod with an OOM error, regardless of how much GPU VRAM is available. This is a common oversight when configuring resource limits.
- ✗
The GPU device plugin is not correctly reporting free memory.
Why it's wrong here
While possible, the device plugin failure would typically prevent the pod from scheduling or result in allocation errors at startup. If the workload is actually executing and then failing with an OOM, it is far more likely to be an application-level memory issue rather than a plugin reporting error.
- ✗
The training job is using mixed-precision training (FP16).
Why it's wrong here
Mixed-precision training is designed to reduce the memory footprint of a model, actually mitigating the risk of OOM errors rather than causing them. It is a standard technique used to fit larger models into limited GPU memory and would not be the cause of an OOM crash.
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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.