This KCNA practice question tests your understanding of cloud native observability. 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.
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?
Prometheus
Prometheus collects metrics and can correlate error rates with resource usage via labels.
Grafana
Why wrong: Grafana visualizes data from sources like Prometheus, but does not store or correlate metrics itself.
Fluentd
Why wrong: Fluentd is a log collector, not a metrics system.
Jaeger
Why wrong: Jaeger is for distributed tracing, not metric correlation.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
Cisco 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.
Detailed technical explanation
Prometheus uses a pull-based model, scraping metrics from HTTP endpoints (typically /metrics) exposed by application pods, and stores them in a custom time-series database with a multi-dimensional data model. The PromQL query `rate(http_requests_total{status=~"5.."}[5m])` can be joined with `container_cpu_usage_seconds_total` to pinpoint when error rates correlate with resource saturation. In real-world scenarios, this helps identify issues like memory leaks causing OOMKilled pods that trigger intermittent 5xx errors.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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What to study next
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FAQ
Cloud Native Observability — This question tests Cloud Native Observability — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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