NCP-AIO Troubleshooting and Optimization Practice Question
If a GPU job consistently fails with 'Out of Memory' despite the model size being significantly smaller than the total VRAM, what is the most likely cause?
⚠ Common exam trap
Candidates frequently assume an OOM error always means total available memory is exhausted, overlooking how memory fragmentation prevents large contiguous allocations.
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
✓
Memory fragmentation in the CUDA context.
Memory fragmentation occurs when frequent allocations and deallocations leave holes in the memory address space. Even if total free memory seems sufficient, large contiguous blocks cannot be allocated. Monitoring fragmentation patterns is essential for AI engineers to optimize memory management, such as using memory pools or persistent buffers, to ensure stable and predictable training performance in long-running jobs.
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 GPU clock speed is too low.
Why it's wrong here
Clock speed affects the speed of computation but has no correlation with the allocation of memory. A slow clock speed would lead to longer training times but would not cause an out-of-memory error if the memory requirement of the model is within the available physical VRAM.
- ✓
Memory fragmentation in the CUDA context.
Why this is correct
Memory fragmentation occurs when the available memory is split into small, non-contiguous blocks. When the model requests a large contiguous block of memory, the allocator fails to find one, resulting in an OOM error even if the sum of all free memory is actually greater than the request.
- ✗
An incompatible version of NCCL library.
Why it's wrong here
NCCL is a library for communication and does not manage the memory allocation of the training job itself. While version incompatibilities can cause crashes, they would typically manifest as segmentation faults or initialization errors, not as 'Out of Memory' errors during the model execution phase.
- ✗
The system bus width is insufficient.
Why it's wrong here
The bus width determines the speed of data transfer between the GPU and other components. It does not dictate the capacity of the memory or how the memory is allocated. Insufficient bandwidth might slow down training, but it would not trigger an OOM error during allocation attempts.
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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.