KCNA Container Orchestration Practice Question
An administrator wants to ensure that a pod only runs on nodes that have a specific GPU. Which mechanism should be used to achieve this?
⚠ Common exam trap
Kubernetes often tests the distinction between node affinity (attracting pods to nodes) and taints/tolerations (repelling pods from nodes), leading candidates to mistakenly choose tolerations when the requirement is to ensure a pod runs only on nodes with a specific resource.
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
✓
Node affinity with requiredDuringSchedulingIgnoredDuringExecution
Node affinity with `requiredDuringSchedulingIgnoredDuringExecution` is the correct mechanism because it allows you to specify hard constraints that a pod must be scheduled on a node with a specific label (e.g., `gpu-type: nvidia-tesla`). This ensures the pod is only placed on nodes that have the required GPU hardware, as the scheduler will enforce the rule during scheduling and ignore it after the pod is running.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Node affinity with requiredDuringSchedulingIgnoredDuringExecution
Why this is correct
Node affinity with requiredDuringSchedulingIgnoredDuringExecution enforces a hard scheduling rule, so the pod is placed only on nodes whose labels match the specified GPU. This satisfies the constraint that the pod must run exclusively on GPU-equipped nodes.
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Tolerations and taints
Why it's wrong here
Taints and tolerations let a pod tolerate a node's taint, but toleration does not require placement on GPU nodes and permits scheduling elsewhere. It is tempting because both govern scheduling eligibility, yet the correct mechanism pairs node affinity with GPU labels.
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Pod anti-affinity
Why it's wrong here
Pod anti-affinity repels pods from nodes matching label selectors based on other pods' placement; it cannot select nodes by hardware attribute such as a GPU. It is tempting because it controls scheduling, but its axis is inter-pod co-location, not node capability.
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ResourceQuota
Why it's wrong here
ResourceQuota caps aggregate CPU, memory, pod or object consumption within a namespace; it never influences which node a pod lands on. It is tempting because both are namespace-level scheduling-adjacent constructs, yet quota enforcement is admission control, not node selection.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This KCNA 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 KCNA exam.