NCP-AIO Troubleshooting and Optimization Practice Question
During a multi-GPU training job, you notice that one GPU consistently reports lower utilization and longer communication times compared to others. What is the most likely reason for this performance imbalance?
⚠ Common exam trap
Candidates often attribute performance imbalances to software bugs or uneven data batching, failing to account for physical hardware topology issues, such as mismatched PCIe lanes or NVLink connectivity.
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 GPU is connected via a slower interconnect.
In multi-GPU systems, uneven load distribution often stems from mismatched NVLink topologies or PCIe lane configurations. If one GPU is connected via a slower PCIe link rather than a high-speed NVLink interconnect, it becomes the bottleneck in collective communication operations like AllReduce. Ensuring symmetric connectivity across all GPUs is essential for predictable performance and preventing the 'straggler' effect in distributed deep learning training.
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 is defective and needs replacement.
Why it's wrong here
Hardware defects usually manifest as errors or hard failures, not consistent performance degradation in communication. Before assuming a hardware failure, the topology and interconnect configuration must be verified. A defective GPU would likely cause the entire job to crash rather than just slowing down communication tasks relative to others.
- ✓
The GPU is connected via a slower interconnect.
Why this is correct
If one GPU in a cluster lacks the high-speed NVLink connection enjoyed by the others, it will be limited by the bandwidth of the slower interface (like PCIe). During AllReduce operations, the entire cluster must wait for this 'straggler' to complete, leading to lower total utilization and increased latency.
- ✗
The training framework is not using CUDA streams.
Why it's wrong here
CUDA streams are standard in modern frameworks and are not the cause of localized performance drops on a single GPU. If streams were not being used, performance would be low across all GPUs, not just one. The issue described is specific to a single node or GPU within the cluster.
- ✗
The ambient server temperature is too high.
Why it's wrong here
If the temperature were the issue, the GPU would throttle and show thermal warning flags in telemetry. While possible, interconnect bottlenecks are a more common cause of specific communication delays in multi-node or multi-GPU systems. Interconnect topology should be verified before jumping to environmental cooling conclusions for single-GPU performance lag.
About these practice questions
Courseiva writes every NCP-AIO question from scratch — 309 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.