NCP-AIO Troubleshooting and Optimization Practice Question
Network Topology
Refer to the exhibit. The system has two GPUs. What is the most likely cause of the observed performance discrepancy?
⚠ Common exam trap
Candidates often guess hardware failure or driver mismatch, when the most common issue is a simple failure to properly initialize or distribute workloads across both available GPUs in the application code.
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
✓
Only one GPU is receiving the workload due to improper data distribution.
The exhibit shows one GPU heavily utilized while the second is idling despite similar memory consumption. This indicates a data parallelism imbalance, where one process is doing the bulk of the work. This is a common issue in multi-GPU setups where workload distribution is not correctly handled, leading to massive inefficiencies where expensive hardware is under-utilized, significantly increasing the time required for model training or inference tasks.
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 GPUs have different amounts of physical memory.
Why it's wrong here
If the GPUs had different physical memory, the system would likely throw an error or fail to initialize the distributed environment. The memory usage is similar, which suggests the hardware is physically capable, but the software is failing to schedule compute tasks across both devices effectively.
- ✓
Only one GPU is receiving the workload due to improper data distribution.
Why this is correct
The discrepancy between high GPU utilization on one device and low utilization on the other strongly suggests that only one GPU is performing the compute-intensive training loop. This is typical when the DataParallel or DistributedDataParallel wrapper is not correctly configured across all available devices.
- ✗
The system is limited by the PCIe bus bandwidth.
Why it's wrong here
PCIe bandwidth issues would cause both GPUs to show low utilization while waiting for data. In this scenario, one GPU is performing at near-maximum capacity, which proves the bus can handle the data load; the bottleneck is specifically the application logic distributing tasks to only one device.
- ✗
The model is too small to be parallelized across two GPUs.
Why it's wrong here
Even small models can be parallelized using techniques like batch parallelism. The issue here is not the model size, but rather the execution environment. The presence of high memory usage on both GPUs suggests the model is loaded on both, but the compute is not being distributed.
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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.