This KCNA practice question tests your understanding of cloud native observability. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are an SRE managing a Kubernetes cluster with 200 nodes and 10,000 pods. The cluster runs a critical payment processing application. Users report that transactions are occasionally failing with a 'timeout' error. You have Prometheus and Grafana set up for monitoring, and you use Fluentd with Elasticsearch for logging. You notice that during peak hours, the CPU usage of the payment service pods spikes to 90%, but memory usage remains stable. The pod restart count is low. You also see that the response time of the payment service increases significantly during these spikes. You need to identify the root cause and propose a fix. Which course of action is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the CPU limits for the payment service pods to allow more CPU resources during spikes
Option D is correct because the CPU usage spikes to 90% during peak hours, indicating that the payment service pods are CPU-bound. Increasing CPU limits allows the pods to burst and utilize more CPU resources, reducing response times and preventing timeouts. This directly addresses the bottleneck without adding unnecessary replicas or changing memory settings.
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.
✗
Add more replicas of the payment service to distribute the load
Why it's wrong here
Horizontal scaling helps but may not address the root cause if the service is inefficient; also adds complexity.
✗
Increase the memory limits for the payment service pods to improve caching
Why it's wrong here
Memory is stable; increasing memory won't help CPU-bound issues.
✗
Implement a circuit breaker pattern to fail fast and avoid timeouts
Why it's wrong here
Circuit breaker prevents cascading failures but doesn't fix the underlying CPU issue.
✓
Increase the CPU limits for the payment service pods to allow more CPU resources during spikes
Why this is correct
This directly addresses the CPU bottleneck, reducing response time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CNCF often tests the misconception that scaling replicas always solves performance issues, but here the bottleneck is per-pod CPU limits, not overall load distribution.
Detailed technical explanation
How to think about this question
In Kubernetes, CPU limits are enforced via the Completely Fair Scheduler (CFS) quota, which throttles pods when they exceed their CPU limit. When a pod hits its CPU limit, requests are queued, increasing response times and causing timeouts. By increasing the CPU limit, you allow the pod to consume more CPU time during bursts, reducing queue depth and latency. This is distinct from CPU requests, which guarantee minimum resources; limits cap maximum usage.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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 Observability — This question tests Cloud Native Observability — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the CPU limits for the payment service pods to allow more CPU resources during spikes — Option D is correct because the CPU usage spikes to 90% during peak hours, indicating that the payment service pods are CPU-bound. Increasing CPU limits allows the pods to burst and utilize more CPU resources, reducing response times and preventing timeouts. This directly addresses the bottleneck without adding unnecessary replicas or changing memory settings.
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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