NCP-AIO Administration Practice Question
An administrator notices that GPU utilization is high, but throughput in an AI training job remains low. What is the most likely bottleneck?
⚠ Common exam trap
Candidates often assume that high GPU utilization is a sign of a healthy, efficient training job, failing to recognize that it can actually indicate the GPU is idling while waiting for data.
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
✓
Data pipeline or storage I/O bottleneck
When GPU utilization is high but throughput is low, the GPU is likely stalling while waiting for data. This is typically a sign of an input/output (I/O) bottleneck, where the data pipeline (e.g., loading images from disk or network) cannot keep up with the GPU's processing speed. Ensuring the data preprocessing pipeline is sufficiently parallelized and optimized is crucial for maximizing GPU utilization and maintaining high training throughput in AI workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Insufficient CUDA cores on the GPU
Why it's wrong here
If the GPU lacked sufficient compute power, the utilization metrics would reflect this by staying at 100% while the job makes slow progress. However, this is a hardware limitation rather than a bottleneck, and the phrasing suggests an efficiency issue related to data pipeline starvation rather than raw compute capacity.
- ✓
Data pipeline or storage I/O bottleneck
Why this is correct
High GPU utilization accompanied by low throughput indicates the GPU is spending time waiting for data to arrive from the CPU or storage. This starvation effect is a classic symptom of an inefficient data loader or slow storage subsystem, which limits the overall throughput despite the GPU's apparent activity.
- ✗
Incompatible NVIDIA driver version
Why it's wrong here
Driver incompatibilities usually cause immediate failures or crashes, not performance bottlenecks. If the driver were fundamentally incompatible with the CUDA runtime or hardware, the job would fail to initialize or execute, rather than running at reduced throughput with high GPU utilization metrics reported by the system monitor.
- ✗
Excessive usage of GPU registers
Why it's wrong here
Register pressure is a low-level kernel optimization issue that affects how occupancy is managed on the SMs. While it can reduce performance, it would typically show as lower GPU utilization because the SMs are unable to launch enough warps, contradicting the scenario where GPU utilization is reported as high.
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.