KCNA Cloud Native Observability Practice Question
A company deploys a microservice application on Kubernetes. They notice that one of the services is returning 5xx errors intermittently. Which observability tool should they use to correlate the errors with resource usage across all pods of that service?
⚠ Common exam trap
The KCNA exam often tests the distinction between observability pillars (metrics, logs, traces) and their specific tools, so the trap here is confusing Grafana (a visualization layer) with Prometheus (a metrics backend) or assuming Jaeger (tracing) can correlate resource usage metrics.
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
✓
Prometheus
Prometheus is the correct choice because it is a monitoring and alerting toolkit designed to collect and store time-series metrics, such as CPU, memory, and request error rates. By querying Prometheus with PromQL, you can correlate 5xx error spikes with resource usage across all pods of a service, as it scrapes metrics from each pod's /metrics endpoint. This direct correlation of application-level errors with infrastructure metrics is not natively provided by the other tools listed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Prometheus
Why this is correct
Prometheus collects metrics and can correlate error rates with resource usage via labels.
- ✗
Grafana
Why it's wrong here
Grafana visualizes data from sources like Prometheus, but does not store or correlate metrics itself.
- ✗
Fluentd
Why it's wrong here
Fluentd is a log collector, not a metrics system.
- ✗
Jaeger
Why it's wrong here
Jaeger is for distributed tracing, not metric correlation.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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