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CKA Storage Practice Question

A StatefulSet named 'web' uses volumeClaimTemplates to create a PVC named 'data' with storage class 'ssd'. The StatefulSet has 3 replicas. After scaling down to 2 replicas, what happens to the PVC of the third pod (web-2)?

⚠ Common exam trap

Many exam-takers assume PVCs are ephemeral like pods or that the reclaim policy controls PVC lifecycle, but in StatefulSets, PVCs persist after pod deletion to preserve state, and only manual deletion or a separate cleanup process removes them.

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

✓

The PVC remains and must be manually deleted if not needed

When a StatefulSet is scaled down, the associated pods are terminated, but the PVCs created via volumeClaimTemplates are not automatically deleted. This is by design to prevent accidental data loss, as the PVCs may contain stateful data that should persist even if the pod is removed. Therefore, the PVC for web-2 remains in the cluster and must be manually deleted if it is no longer needed.

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 PVC is detached but not deleted

    Why it's wrong here

    When a StatefulSet pod is scaled down or deleted, the PersistentVolumeClaim (PVC) remains bound to its corresponding PersistentVolume (PV). The PVC is not actively detached or unbound from the PV by the control plane during this scale-down event. Instead, the binding persists to ensure that if the pod is scaled back up, it can immediately reattach to the exact same storage volume and maintain state.

  • ✓

    The PVC remains and must be manually deleted if not needed

    Why this is correct

    Kubernetes intentionally preserves PersistentVolumeClaims created by StatefulSet VolumeClaimTemplates to prevent accidental data loss during scale-down operations. Because the lifecycle of the PVC is decoupled from the individual Pod lifecycle, an administrator must explicitly delete the PVCs manually if the data is no longer required. This safety mechanism ensures stateful data is never deleted without explicit administrative intent.

  • ✗

    The PVC's reclaim policy determines its fate

    Why it's wrong here

    Reclaim policies (such as Retain, Recycle, or Delete) are configuration attributes of the PersistentVolume (PV) resource, not the PersistentVolumeClaim (PVC). Even if a PV's reclaim policy is set to Delete, this policy only triggers after the binding PVC itself has been deleted. The PVC itself will always persist when a StatefulSet scales down, regardless of any PV reclaim configuration.

  • ✗

    The PVC is automatically deleted along with the pod

    Why it's wrong here

    Unlike stateless workloads where ephemeral storage is destroyed with the pod, StatefulSets are designed to guarantee stable storage. Automatically deleting the PVC alongside the pod would result in catastrophic data loss during routine scaling or rolling updates. Therefore, the Kubernetes controller manager explicitly excludes PVCs from the garbage collection cascade when a StatefulSet pod is terminated.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This CKA practice question is part of Courseiva's free CNCF 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 CKA exam.