NCP-AIO Workload Management Practice Question
A platform team runs mixed training and inference workloads on a Kubernetes cluster with the NVIDIA GPU Operator. Inference pods are latency-sensitive and must not be preempted, while training pods can be interrupted and restarted. The team wants training jobs to yield GPUs to inference jobs when capacity is scarce, without manual intervention. Which Kubernetes mechanism should the team configure to achieve this behavior?
⚠ Common exam trap
Candidates often confuse PodDisruptionBudget, which limits voluntary evictions, with preemption, which actively evicts lower-priority pods to place a higher-priority one.
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
✓
PriorityClass with preemptionPolicy set to PreemptLowerPriority on the inference pods.
Priority and preemption are the scheduler features designed for exactly this pattern. Assigning inference pods a higher-priority class with preemption enabled lets the scheduler evict lower-priority training pods when a node lacks free GPUs. Because training pods are restartable, the disruption is acceptable, and inference keeps its latency guarantees without manual intervention.
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 PodDisruptionBudget on the training pods that guarantees a minimum number of running replicas.
Why it's wrong here
A PodDisruptionBudget constrains voluntary disruptions such as node drains, but it does not make the scheduler preempt lower-priority pods to place a higher-priority one. It also protects the training workload from being evicted, which is the opposite of the desired behavior. It cannot cause training pods to yield GPUs to inference pods under capacity pressure.
- ✓
PriorityClass with preemptionPolicy set to PreemptLowerPriority on the inference pods.
Why this is correct
PriorityClass assigns a numeric priority, and when a high-priority pod cannot schedule, the scheduler may evict lower-priority pods on a node to make room. Setting the inference pods to a higher priority with preemption enabled lets them displace training pods, which can restart. This directly implements automatic yielding of GPUs to latency-sensitive inference workloads without manual operator action.
- ✗
ResourceQuota on the training namespace limiting total GPU requests below cluster capacity.
Why it's wrong here
A ResourceQuota caps consumption within a namespace but does not trigger eviction or reallocation when another workload needs resources. If training is already within quota, nothing changes. Quota enforcement rejects new pods that exceed the limit; it has no mechanism to reclaim GPUs from running training pods for the benefit of inference pods.
- ✗
A node affinity rule on the inference pods that targets nodes with the highest available GPU memory.
Why it's wrong here
Node affinity influences which nodes a pod prefers or requires, but it does not evict running pods to free capacity. If the preferred nodes are fully consumed by training pods, the inference pods simply wait. Node affinity alone cannot produce the preemption behavior needed to make training jobs yield GPUs automatically.
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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.