KCNA Cloud Native Observability Practice Question
A company runs a Kubernetes cluster with 50 worker nodes, each hosting multiple microservices. They use Prometheus for metrics collection and Grafana for dashboards. Recently, the Prometheus server has been experiencing out-of-memory (OOM) kills during peak hours, causing gaps in metric collection. The cluster has a dedicated monitoring namespace. The team has already increased the Prometheus pod's memory limits to 8GB, but OOMs still occur. The metrics retention is set to 15 days. The cardinality of certain metrics (e.g., HTTP request labels with user IDs) is very high. The team needs to resolve the OOM issue without losing critical alerting capability for at least the last 7 days of data. Which action should they take first?
⚠ Common exam trap
Test-takers frequently confuse memory pressure (caused by cardinality) with storage pressure (caused by retention), leading candidates to incorrectly choose reducing retention (Option C) instead of addressing the root cause of high cardinality via recording rules.
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
✓
Implement recording rules to pre-aggregate high-cardinality metrics at a lower granularity
Recording rules allow Prometheus to pre-aggregate high-cardinality metrics (e.g., HTTP request labels with user IDs) at a lower granularity, reducing the number of unique time series stored in memory. This directly addresses the OOM issue caused by cardinality explosion without discarding raw data entirely, preserving the ability to query aggregated metrics for alerting over the required 7-day window.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement recording rules to pre-aggregate high-cardinality metrics at a lower granularity
Why this is correct
Recording rules reduce cardinality by aggregating metrics, lowering memory usage while preserving aggregated data for alerting.
- ✗
Drop high-cardinality metrics like HTTP request labels using relabel_configs
Why it's wrong here
This may remove useful metrics; better to aggregate them.
- ✗
Reduce metrics retention to 7 days to free memory
Why it's wrong here
Reduces memory but also loses older data; less targeted than recording rules.
- ✗
Enable vertical pod autoscaler for the Prometheus pod
Why it's wrong here
VPA adjusts resources but does not reduce cardinality; OOM may still occur if node is saturated.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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