KCNA Container Orchestration Practice Question
You have a Kubernetes cluster with multiple nodes. You need to ensure that a pod runs on a node that has an SSD. How should you achieve this?
⚠ Common exam trap
Test-takers frequently confuse tolerations (which allow scheduling onto tainted nodes) with node selection mechanisms like nodeSelector or nodeAffinity, leading candidates to pick D when they need a label-based constraint.
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
✓
Use a nodeSelector with the label 'disktype: ssd'
NodeSelector is the simplest and most direct way to constrain a Pod to run only on nodes that have a specific label, such as 'disktype=ssd'. When you add a nodeSelector field to the Pod spec, the kube-scheduler filters nodes that do not have the matching label, ensuring the Pod lands on a node with an SSD.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually edit kube-scheduler configuration
Why it's wrong here
This is complex and unnecessary; nodeSelector or nodeAffinity are the standard approaches.
- ✗
Use a DaemonSet to run the pod on all nodes
Why it's wrong here
DaemonSet runs a pod on every node, not just those with SSDs.
- ✓
Use a nodeSelector with the label 'disktype: ssd'
Why this is correct
nodeSelector is a simple field that matches node labels, making it the easiest way to schedule pods on nodes with SSDs.
- ✗
Use a toleration for the node
Why it's wrong here
Tolerations are used to allow pods to schedule on nodes with taints, not to select specific hardware.
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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.