NCP-AIO Installation and Deployment Practice Question
An administrator is tasked with deploying a multi-node training job using the NVIDIA GPU Operator. Which configuration must be present to ensure that pods are scheduled on nodes with identical GPU architectures to prevent performance degradation?
⚠ Common exam trap
Candidates often rely on default scheduling, forgetting that Kubernetes is unaware of GPU architecture differences. They fail to use NFD labels, leading to heterogeneous nodes that cause significant performance degradation.
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
✓
Use node affinity labels based on NFD-provided hardware information.
To maintain high-performance, synchronized training, all participating nodes should share the same GPU architecture and interconnect type (e.g., NVLink or InfiniBand). Using Kubernetes node affinity or anti-affinity rules combined with the hardware labels automatically generated by the Node Feature Discovery (NFD) service ensures that the scheduler places the job exclusively on compatible nodes. This prevents the training from falling back to slower, sub-optimal communication paths or heterogeneous hardware modes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the 'auto-scaling' feature in the NVIDIA GPU Operator.
Why it's wrong here
Auto-scaling is related to cluster capacity management and adding/removing nodes based on load. It does not provide the logic required to enforce hardware architecture constraints on specific training jobs. Scheduling constraints must be defined by the workload deployment manifest, not by the operator's capacity management features.
- ✓
Use node affinity labels based on NFD-provided hardware information.
Why this is correct
Node Feature Discovery (NFD) labels nodes with their GPU model and architecture. By using these labels in the training pod's affinity configuration, the administrator forces the scheduler to select only nodes that match the desired hardware profile, ensuring consistent performance for distributed training jobs that rely on identical hardware capabilities.
- ✗
Increase the timeout values for the NCCL collective communication operations.
Why it's wrong here
Increasing timeouts is a reactive measure that masks the issue rather than preventing it. It does not ensure hardware homogeneity, which is the root cause of performance degradation in heterogeneous training environments. Proper scheduling based on hardware characteristics is the correct, proactive approach to solving this multi-node synchronization issue.
- ✗
Set the 'nvidia.com/gpu' resource limit to zero on all nodes except the master node.
Why it's wrong here
Setting resource limits to zero effectively disables GPU access on those nodes, preventing them from participating in the training job entirely. This would lead to a scheduling failure or a training job that runs only on the master node, defeating the purpose of a multi-node distributed training deployment.
Visual reference
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.