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.
A company is running a microservices application on a Kubernetes cluster. They have noticed that one of the services, 'payment-api', is experiencing intermittent high latency. The team wants to identify the root cause without modifying the application code. Which approach should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement distributed tracing using tools like Jaeger or Zipkin to trace requests across services.
Option C is correct because distributed tracing with tools like Jaeger or Zipkin allows you to follow a single request as it traverses multiple microservices, identifying exactly which service or call introduces latency. This approach does not require code changes (if the service mesh or sidecar proxy handles instrumentation) and is specifically designed to pinpoint performance bottlenecks in distributed systems, unlike CPU/memory metrics or log analysis which cannot trace a request's end-to-end path.
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.
✗
Monitor CPU and memory metrics from kube-state-metrics and correlate with latency.
Why it's wrong here
Resource metrics may not directly indicate request latency.
✗
Increase log verbosity for all services and search for error messages.
Why it's wrong here
Logs may not capture latency across services.
✓
Implement distributed tracing using tools like Jaeger or Zipkin to trace requests across services.
Why this is correct
Distributed tracing tracks request flow and identifies slow components.
Related concept
Read the scenario before looking for a memorised answer.
✗
Check node-level metrics using Prometheus Node Exporter.
Why it's wrong here
Node metrics are too granular for service-level latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CNCF often tests the distinction between observability tools that provide request-level context (distributed tracing) versus aggregate resource metrics (kube-state-metrics, Node Exporter) or unstructured logs, leading candidates to mistakenly choose CPU/memory correlation or log analysis for pinpointing intermittent latency in a microservices architecture.
Detailed technical explanation
How to think about this question
Distributed tracing works by injecting a unique trace ID into each request at the ingress point, which is then propagated via HTTP headers (e.g., Zipkin B3 propagation or W3C Trace-Context) across all service calls. Each service records spans with start and end timestamps, allowing tools like Jaeger to reconstruct the full call graph and calculate per-span latency. In a real-world scenario, a payment-api service might show high latency only when a downstream fraud-check service times out due to a slow database query, which would be invisible in CPU or log metrics but clearly visible as a single long span in the trace.
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: Implement distributed tracing using tools like Jaeger or Zipkin to trace requests across services. — Option C is correct because distributed tracing with tools like Jaeger or Zipkin allows you to follow a single request as it traverses multiple microservices, identifying exactly which service or call introduces latency. This approach does not require code changes (if the service mesh or sidecar proxy handles instrumentation) and is specifically designed to pinpoint performance bottlenecks in distributed systems, unlike CPU/memory metrics or log analysis which cannot trace a request's end-to-end path.
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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