PCDOE Implementing service monitoring strategies Practice Question
Which TWO metrics should be included in a comprehensive monitoring strategy for a production Kubernetes workload to detect performance degradation and capacity issues?
⚠ Common exam trap
Google Cloud often tests the distinction between infrastructure-level metrics (like node count or network bytes) and application-level metrics (like latency percentiles) that directly measure user experience and workload health.
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
✓
Container CPU utilization
Container CPU utilization (Option B) is a direct indicator of resource pressure and potential performance degradation in a Kubernetes workload. High CPU utilization can lead to throttling, increased request latency, and pod evictions, making it essential for detecting capacity issues. Request latency percentiles (Option E) are the gold standard for measuring user-facing performance degradation, as they reflect the actual experience of end users and can reveal subtle slowdowns before resource metrics show saturation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disk read IOPS per pod
Why it's wrong here
Disk IOPS is important for stateful workloads but not a general performance indicator for all workloads.
- ✓
Container CPU utilization
Why this is correct
High CPU utilization can indicate capacity pressure and performance issues.
- ✗
Number of nodes in the cluster
Why it's wrong here
Node count is an infrastructure metric; it doesn't directly measure workload performance.
- ✗
Network bytes received per second
Why it's wrong here
Network throughput is not a direct measure of performance degradation; it may vary with traffic.
- ✓
Request latency percentiles (e.g., p99)
Why this is correct
Latency percentiles directly reflect user experience and performance degradation.
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