This CKA practice question tests your understanding of workloads & scheduling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An application requires that a pod runs on a node that has a GPU. The cluster has nodes with and without GPUs labeled as 'gpu=true' and 'gpu=false'. Which scheduling method should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
nodeSelector with gpu=true
Option D is correct because `nodeSelector` is the simplest and most direct way to force a pod to run only on nodes that have a specific label, such as `gpu=true`. This ensures the pod is scheduled exclusively on GPU-equipped nodes without requiring taints, tolerations, or complex affinity rules. The `nodeSelector` field in the pod spec matches against node labels at scheduling time, making it ideal for this straightforward requirement.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Taint on non-GPU nodes and toleration on the pod
Why it's wrong here
This would prevent scheduling on non-GPU nodes but does not guarantee scheduling on GPU nodes.
✗
Pod affinity to prefer GPU nodes
Why it's wrong here
Pod affinity is for co-locating pods, not for node selection.
✗
Node affinity with a requiredDuringSchedulingIgnoredDuringExecution rule for gpu=true
Why it's wrong here
This would also work but is more complex than needed for a simple label match.
✓
nodeSelector with gpu=true
Why this is correct
nodeSelector directly matches the label gpu=true.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse taints/tolerations (which repel pods) with node affinity or nodeSelector (which attract pods to specific nodes), leading them to pick Option A when the requirement is to ensure a pod runs on a GPU node, not to prevent it from running on non-GPU nodes.
Detailed technical explanation
How to think about this question
Under the hood, `nodeSelector` is implemented as a simplified form of node affinity that matches against a single label key-value pair. It is evaluated during the `Filter` phase of the Kubernetes scheduler, where nodes without the matching label are excluded from the candidate list. In real-world scenarios, `nodeSelector` is often used for hardware-specific workloads (e.g., GPUs, SSDs) where a simple label match suffices, while node affinity offers more advanced operators like `In`, `NotIn`, and `Exists` for complex scheduling logic.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the CKA exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this CKA question in full detail.
Workloads & Scheduling — This question tests Workloads & Scheduling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: nodeSelector with gpu=true — Option D is correct because `nodeSelector` is the simplest and most direct way to force a pod to run only on nodes that have a specific label, such as `gpu=true`. This ensures the pod is scheduled exclusively on GPU-equipped nodes without requiring taints, tolerations, or complex affinity rules. The `nodeSelector` field in the pod spec matches against node labels at scheduling time, making it ideal for this straightforward requirement.
What should I do if I get this CKA question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.