- A
Set resource requests for 'gpu' in the container spec.
Why wrong: GPUs are not standard resources; they are extended resources and must be requested with their specific name (e.g., nvidia.com/gpu).
- B
Use nodeAffinity with requiredDuringSchedulingIgnoredDuringExecution matching a label that identifies GPU nodes.
Node affinity can force scheduling on nodes with a specific label, such as 'gpu=true'.
- C
Set nodeName to the name of a specific GPU node.
Why wrong: nodeName is too rigid and does not leverage node capabilities; it also does not check for GPU availability.
- D
Add a toleration for a taint that GPU nodes have.
Why wrong: Tolerations allow scheduling on tainted nodes but do not guarantee the node has a GPU.
- E
Set resource requests for 'nvidia.com/gpu' in the container spec and ensure the node has that resource.
This is the standard way to request GPU resources via extended resources.
CKA Workloads & Scheduling Practice Question
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.
Which TWO of the following are valid ways to ensure that a Pod runs on a node that has a GPU? (Choose TWO.)
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 nodeAffinity with requiredDuringSchedulingIgnoredDuringExecution matching a label that identifies GPU nodes.
Option B is correct because nodeAffinity with requiredDuringSchedulingIgnoredDuringExecution allows you to schedule a Pod only onto nodes that match specific labels, such as a label like 'gpu=true' that identifies GPU-equipped nodes. This ensures the Pod is placed on a node with a GPU without relying on resource requests or taints.
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.
- ✗
Set resource requests for 'gpu' in the container spec.
Why it's wrong here
GPUs are not standard resources; they are extended resources and must be requested with their specific name (e.g., nvidia.com/gpu).
- ✓
Use nodeAffinity with requiredDuringSchedulingIgnoredDuringExecution matching a label that identifies GPU nodes.
Why this is correct
Node affinity can force scheduling on nodes with a specific label, such as 'gpu=true'.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set nodeName to the name of a specific GPU node.
Why it's wrong here
nodeName is too rigid and does not leverage node capabilities; it also does not check for GPU availability.
- ✗
Add a toleration for a taint that GPU nodes have.
Why it's wrong here
Tolerations allow scheduling on tainted nodes but do not guarantee the node has a GPU.
- ✓
Set resource requests for 'nvidia.com/gpu' in the container spec and ensure the node has that resource.
Why this is correct
This is the standard way to request GPU resources via extended resources.
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 resource requests for custom resources (like 'gpu') with the correct extended resource name (e.g., 'nvidia.com/gpu'), or they think a toleration alone is sufficient to guarantee a node has a GPU, when it only allows scheduling onto tainted nodes without ensuring the required hardware is present.
Detailed technical explanation
How to think about this question
Extended resources like 'nvidia.com/gpu' must be advertised by the node via the device plugin framework and are counted in the node's capacity; setting resource requests for them ensures the scheduler only places the Pod on nodes with sufficient available GPUs. Node affinity uses label matching and is evaluated by the scheduler during pod placement, while taints and tolerations control which Pods can be scheduled on a node but do not enforce resource availability. In practice, a GPU node might be tainted to reserve it for GPU workloads, requiring both a toleration and either node affinity or resource requests to ensure correct scheduling.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Workloads & Scheduling — study guide chapter
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FAQ
Questions learners often ask
What does this CKA question test?
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: Use nodeAffinity with requiredDuringSchedulingIgnoredDuringExecution matching a label that identifies GPU nodes. — Option B is correct because nodeAffinity with requiredDuringSchedulingIgnoredDuringExecution allows you to schedule a Pod only onto nodes that match specific labels, such as a label like 'gpu=true' that identifies GPU-equipped nodes. This ensures the Pod is placed on a node with a GPU without relying on resource requests or taints.
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 →
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Last reviewed: Jun 11, 2026
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.
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