KCNA Container Orchestration Practice Question
A developer wants to ensure that a pod runs only on nodes with SSDs. Which mechanism should be used?
⚠ Common exam trap
Test-takers frequently confuse taints/tolerations with node selection, thinking they can be used to force pods onto specific hardware, when in fact taints repel pods and tolerations allow exceptions, whereas `nodeSelector` or `nodeAffinity` are the correct tools for positive selection.
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
✓
Add a nodeSelector with disktype: ssd
`nodeSelector` is a simple and direct mechanism in Kubernetes to constrain a pod to run only on nodes that have a specific label, such as `disktype=ssd`. By labeling nodes with SSDs and adding the corresponding `nodeSelector` in the pod spec, the scheduler ensures the pod is placed exclusively on those nodes. This approach is straightforward and does not require complex scheduling constraints or resource management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a taint to nodes without SSDs and add tolerations to the pod
Why it's wrong here
Taints are used to repel pods; adding tolerations to all pods would not restrict to SSD nodes.
- ✗
Use pod anti-affinity
Why it's wrong here
Anti-affinity spreads pods across nodes, not based on node labels.
- ✓
Add a nodeSelector with disktype: ssd
Why this is correct
nodeSelector ensures pods are scheduled on nodes with the specified label.
- ✗
Define a ResourceQuota
Why it's wrong here
ResourceQuota limits resource consumption, not node placement.
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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.