NCP-AIO Workload Management Practice Question
A financial services firm must prove to auditors that an AI training job ran on hardware located only in its Frankfurt data center and that no pod could ever be scheduled onto GPUs in other regions. The cluster spans three regions with nodes labeled topology.kubernetes.io/region. Which approach most directly enforces this placement requirement?
⚠ Common exam trap
A common mix-up: candidates confuse soft preferred affinity, which the scheduler may ignore under pressure, with required affinity, which is a hard constraint that keeps the pod Pending instead of violating the rule.
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
✓
Define a nodeAffinity rule in the pod spec requiring topology.kubernetes.io/region to equal eu-central-1, and mark it requiredDuringSchedulingIgnoredDuringExecution.
Hard placement guarantees come from required node affinity, which makes matching labels a precondition for scheduling. Because the scheduler will not place the pod on any node whose region label differs, the job cannot leave the Frankfurt data center, giving the firm an enforceable, auditable control over GPU location.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define a nodeAffinity rule in the pod spec requiring topology.kubernetes.io/region to equal eu-central-1, and mark it requiredDuringSchedulingIgnoredDuringExecution.
Why this is correct
A required node affinity rule is a hard constraint: the kube-scheduler will only place the pod on nodes whose labels match, and it leaves the pod Pending rather than scheduling it elsewhere. Pinning the region label guarantees the training job runs exclusively in the Frankfurt nodes, which is the enforceable evidence auditors need.
- ✗
Add a preferredDuringSchedulingIgnoredDuringExecution nodeAffinity rule that favors the Frankfurt region with a high weight.
Why it's wrong here
Preferred affinity is only a soft hint; if Frankfurt capacity is unavailable, the scheduler will happily place the pod on a node in another region. That behavior directly violates the requirement that the job must never run outside Frankfurt, so a weighted preference cannot satisfy the audit control.
- ✗
Set the pod's restartPolicy to Never and add a toleration for the region-specific taint on Frankfurt nodes.
Why it's wrong here
A toleration merely allows a pod to be placed on a tainted node; it does not require that placement, so the scheduler could still choose an untainted node in another region. Changing restartPolicy affects failure handling only and provides no geographic guarantee, leaving the audit requirement unmet.
- ✗
Create a PodDisruptionBudget for the training job that limits voluntary evictions to zero during the run.
Why it's wrong here
A PodDisruptionBudget controls voluntary disruptions such as node drains; it has no influence on where the scheduler initially places a pod. The training job could still be scheduled in another region at admission time, so this mechanism does nothing to enforce the geographic restriction the auditors require.
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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.