NCP-AIO Administration Practice Question
Exhibit
Error Log: [NCCL] NCCL_P2P_DISABLE=1 detected. Using network-based communication instead of NVLink.
Refer to the exhibit. An administrator observes this in the logs during a multi-GPU training job. What is the performance implication of this setting?
⚠ Common exam trap
Candidates often assume that if a training job runs, it is running optimally, failing to realize that disabling P2P forces traffic over the slower PCIe bus instead of high-speed NVLink.
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
✓
GPU-to-GPU data transfer performance is significantly reduced
Disabling Peer-to-Peer (P2P) communication forces NCCL to use system memory (often via the network or PCIe bus) to facilitate data movement between GPUs. This bypasses the high-bandwidth NVLink interconnect, resulting in significantly increased latency and lower throughput for collective operations. This configuration is typically used only for debugging or when hardware incompatibility prevents direct P2P access, as it severely hinders the performance of multi-GPU, multi-node AI training 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.
- ✗
The training job will have lower memory consumption
Why it's wrong here
Disabling P2P does not reduce memory usage; in some cases, it may actually increase it due to the need for additional staging buffers in system RAM. The primary impact is on communication latency and bandwidth, not on the total memory footprint of the application or the model parameters.
- ✓
GPU-to-GPU data transfer performance is significantly reduced
Why this is correct
By disabling P2P, the system is forced to move data through the PCIe bus or system memory, which is significantly slower than using NVLink or direct P2P access. This causes a major bottleneck in collective operations like AllReduce, which are fundamental to the scalability of distributed AI training jobs.
- ✗
The job will be more stable across nodes
Why it's wrong here
Stability is not improved by disabling P2P; in fact, relying on network-based communication increases the susceptibility to network congestion and latency jitter. This setting is a performance workaround, not a reliability feature, and it does not enhance the robustness of the training job against network or node-level failures.
- ✗
The system will utilize less power during training
Why it's wrong here
While bypassing NVLink might theoretically reduce the power consumed by the interconnect, the performance penalty is so severe that the total energy efficiency of the training job decreases significantly. The job takes longer to complete, leading to higher overall power consumption for the same amount of computation.
About these practice questions
One of 309 original NCP-AIO practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.