KCNA Cloud Native Observability Practice Question
A platform team runs a 12-node Kubernetes cluster where each node hosts roughly 30 pods. They deployed Prometheus with a ServiceMonitor that scrapes every pod's /metrics endpoint every 15 seconds, but now the Prometheus pod is frequently OOMKilled and scrape targets intermittently report 'context deadline exceeded'. Which change best addresses the root cause while preserving observability?
⚠ Common exam trap
The trap here is assuming that more memory, a shorter scrape interval, or remote write storage solves Prometheus overload, when the actual driver is uncontrolled cardinality and target count.
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
✓
Reduce scrape scope using relabeling and metricRelabelings to drop unused targets and high-cardinality metrics, and split scraping across multiple Prometheus shards.
The symptoms point to scrape and cardinality overload: too many targets and time series for a single Prometheus instance. Trimming what is scraped through relabeling and metricRelabelings reduces active series, and sharding spreads the remaining scrape load across multiple servers. Together these address both the memory exhaustion and the scrape deadline errors without abandoning observability of essential metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the Prometheus container's memory limit and CPU request so it can hold all active time series in memory.
Why it's wrong here
Raising the limit may postpone the OOMKill, but the underlying cardinality and scrape volume remain unbounded, so memory growth continues until the pod is killed again. It also consumes node resources that could otherwise run workloads, and it does nothing to explain the scrape timeouts, which stem from too many concurrent targets rather than insufficient CPU alone.
- ✗
Shorten the scrape interval to 5 seconds so each scrape collects fewer samples and finishes before the deadline.
Why it's wrong here
Shorter intervals increase, not decrease, the number of concurrent scrapes, samples ingested per second, and memory pressure. Each scrape still returns the same full metric payload, so the deadline errors would worsen. This directly aggravates the OOMKill symptom and is the opposite of the capacity relief the cluster needs.
- ✗
Enable the Prometheus remote write receiver and forward all samples to a long-term storage backend such as Thanos or Cortex.
Why it's wrong here
Remote write offloads long-term retention but the Prometheus server still scrapes, parses, and holds all samples in its head block before shipping them. If scraping every pod every 15 seconds already exceeds its memory budget, remote write does not reduce the ingest and cardinality pressure causing the OOMKill or the scrape timeouts.
- ✓
Reduce scrape scope using relabeling and metricRelabelings to drop unused targets and high-cardinality metrics, and split scraping across multiple Prometheus shards.
Why this is correct
The cluster-wide scrape of every pod creates excessive active time series and concurrent target load, which drives memory usage and scrape deadline errors. Dropping unneeded targets and metrics through relabeling lowers cardinality, while sharding distributes scrape work across multiple Prometheus instances so no single server is overwhelmed, preserving observability for the metrics that matter.
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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
This KCNA practice question is part of Courseiva's free CNCF certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the KCNA exam.