KCNA Cloud Native Application Delivery Practice Question
This KCNA practice question tests your understanding of cloud native application delivery. 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.
Your organization runs a microservices application on a Kubernetes cluster with 5 worker nodes (each with 4 vCPU, 16GB RAM). The application consists of 20 microservices, each deployed as a Deployment with 3 replicas. Recently, after a new microservice 'inventory' was deployed with resource requests of 2 CPU and 4GB memory per pod, the cluster started experiencing pod scheduling failures. Many existing pods are in 'Pending' state with events indicating 'Insufficient cpu' or 'Insufficient memory'. The cluster has cluster autoscaling enabled (node pool ranging from 3 to 10 nodes), but new nodes are not being added quickly enough, and the existing nodes are heavily utilized. You need to resolve the scheduling failures while ensuring the inventory service can scale. Which course of action should you take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the CPU request of the inventory deployment to 1 CPU per pod to allow better packing on existing nodes while cluster autoscaler catches up.
Option C is correct because reducing the CPU request of the inventory deployment to 1 CPU per pod allows the scheduler to pack pods more efficiently on existing nodes, alleviating immediate 'Insufficient cpu' and 'Insufficient memory' failures while the cluster autoscaler provisions new nodes. This approach balances short-term scheduling needs with the ability to scale the inventory service later, as requests can be adjusted upward once the cluster has more capacity.
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.
✗
Increase the cluster autoscaler max nodes to 20 and set a 0-second scale-up delay.
Why it's wrong here
Does not solve the immediate packing issue; also 0-second delay may cause rapid scaling.
✗
Set resource limits equal to requests for all microservices to guarantee resources.
Why it's wrong here
Does not free up resources; may cause more contention if limits are high.
✓
Reduce the CPU request of the inventory deployment to 1 CPU per pod to allow better packing on existing nodes while cluster autoscaler catches up.
Why this is correct
Lowering requests improves packing and reduces pending status immediately.
Related concept
Read the scenario before looking for a memorised answer.
✗
Delete all pending pods and recreate them manually.
Why it's wrong here
Temporary fix that does not address resource constraints.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think increasing cluster autoscaler limits or setting limits equal to requests will solve the problem, but they overlook that the autoscaler cannot instantaneously add nodes and that setting limits does not free up existing resources, while reducing requests directly addresses the immediate scheduling bottleneck.
Detailed technical explanation
How to think about this question
Kubernetes scheduling decisions are based on pod resource requests, not limits, and the scheduler uses a bin-packing algorithm to place pods on nodes with sufficient allocatable resources. Reducing the CPU request from 2 to 1 effectively halves the resource footprint per inventory pod, allowing the scheduler to fit more pods on existing nodes and reducing the time to schedule pending pods. In a real-world scenario, this approach is often combined with vertical pod autoscaling (VPA) to dynamically adjust requests based on actual usage once the cluster stabilizes.
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 KCNA 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 KCNA question in full detail.
Cloud Native Application Delivery — This question tests Cloud Native Application Delivery — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the CPU request of the inventory deployment to 1 CPU per pod to allow better packing on existing nodes while cluster autoscaler catches up. — Option C is correct because reducing the CPU request of the inventory deployment to 1 CPU per pod allows the scheduler to pack pods more efficiently on existing nodes, alleviating immediate 'Insufficient cpu' and 'Insufficient memory' failures while the cluster autoscaler provisions new nodes. This approach balances short-term scheduling needs with the ability to scale the inventory service later, as requests can be adjusted upward once the cluster has more capacity.
What should I do if I get this KCNA 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.
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Question Discussion
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