NCP-AIO Workload Management Practice Question
A researcher submits a distributed training job that spans four pods, each needing one GPU, and the pods must start together or not at all. The administrator wants Kubernetes to schedule all four pods only when four GPUs are simultaneously available. Which workload management construct should be used?
⚠ Common exam trap
The trap here is assuming that a standard Job or StatefulSet provides atomic, all-or-nothing scheduling, when in fact only gang-scheduling constructs enforce that guarantee.
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
✓
A PodGroup managed by a scheduler that supports gang scheduling, such as Volcano or the scheduler-plugins coscheduling plugin.
Distributed training needs gang scheduling so that either every worker pod is placed or none is. A PodGroup interpreted by a gang-aware scheduler like Volcano or the coscheduling plugin holds the pods until the full set of GPUs is available, avoiding deadlocks and wasted accelerator time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Kubernetes Job with parallelism set to 4 and completions set to 4.
Why it's wrong here
A Job controls how many pods run and complete, but it does not provide gang scheduling. Pods are created and scheduled independently, so some may start while others remain pending for lack of GPUs, which violates the all-or-nothing requirement for the distributed training job.
- ✗
A StatefulSet with podManagementPolicy set to Parallel.
Why it's wrong here
Parallel pod management only affects the order in which a StatefulSet creates and deletes its pods; it does not coordinate scheduling across them. Pods can still be admitted one at a time as GPUs free up, so the all-or-nothing requirement is not met.
- ✗
A DaemonSet that places one training pod on each GPU node in the cluster.
Why it's wrong here
A DaemonSet runs one pod per matching node and is designed for node-level agents, not coordinated multi-pod jobs. It offers no all-or-nothing scheduling guarantee and would run pods on every eligible node regardless of whether the distributed job can proceed, wasting GPU capacity.
- ✓
A PodGroup managed by a scheduler that supports gang scheduling, such as Volcano or the scheduler-plugins coscheduling plugin.
Why this is correct
Gang scheduling ensures that all pods in a PodGroup are scheduled together or none are, preventing partial starts that waste GPUs and stall distributed training. This directly satisfies the requirement that the four GPU pods begin only when four GPUs are simultaneously available.
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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.