KCNA Cloud Native Observability Practice Question
A company defines an SLO that 99.9% of requests to a service should complete in under 200ms. Which metric type is used to measure this SLO?
⚠ Common exam trap
The trap is choosing Summary because it also measures latency distributions; the key differentiator is that Summaries cannot be aggregated across instances, which breaks service-wide SLO calculations.
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
✓
Histogram
A histogram is the Prometheus metric type designed to capture the distribution of observations (like request durations) into configurable buckets, and it automatically exposes _bucket, _sum, and _count series. This allows you to compute quantiles (e.g., the 99.9th percentile latency) and ratios such as 'fraction of requests under 200ms' using PromQL, which is exactly what the SLO requires.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Summary
Why it's wrong here
A summary calculates client-side quantiles, but those cannot be aggregated across instances to evaluate the 99.9% threshold reliably. Summaries suit per-instance latency inspection; the SLO needs histogram buckets aggregated server-side to compute the proportion under 200ms.
- ✓
Histogram
Why this is correct
Histograms bucket observations into configurable ranges, so the proportion of requests completing under 200ms can be calculated from the relevant bucket counts. This satisfies the SLO's latency threshold, which a gauge or counter cannot express as a distribution.
- ✗
Gauge
Why it's wrong here
A gauge records an instantaneous value that rises and falls, so it cannot compute the 99.9% proportion of requests under 200ms. Gauges suit current CPU usage or active connections, making them the right pick for point-in-time readings rather than distribution-based SLOs.
- ✗
Counter
Why it's wrong here
A counter only ever increases, so it cannot express the proportion of requests falling under 200ms. It is tempting because counters do track totals such as request counts, which would be the right choice for measuring raw throughput or error volume, not a latency threshold SLO.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CNCF exam blueprint
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