This KCNA practice question tests your understanding of cloud native observability. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue words "first", "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing 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.
Detailed technical explanation
How to think about this question
Prometheus memory usage is proportional to the number of active time series (unique metric + label combinations), not just data volume. High-cardinality labels like user IDs can create millions of series, exhausting memory even with 8GB limits. Recording rules aggregate these series into lower-cardinality metrics (e.g., sum by (endpoint) instead of sum by (user_id)), reducing memory pressure while preserving aggregated alerting data. Under the hood, Prometheus stores each series in a memory-mapped chunk; reducing series count directly reduces the chunk overhead.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the KCNA exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this KCNA question in full detail.
Cloud Native Observability — This question tests Cloud Native Observability — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement recording rules to pre-aggregate high-cardinality metrics at a lower granularity — Option A is correct because 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.
What should I do if I get this KCNA question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first", "least". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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