Google PCA Ensure solution and operations reliability Practice Question
A developer wants to monitor a custom application metric from their application running on GKE. What should they use?
⚠ Common exam trap
A common mix-up: candidates confuse Cloud Logging (for logs) with Cloud Monitoring (for metrics), or assume that Cloud Trace can handle custom metrics because it deals with application performance data.
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
✓
Cloud Monitoring custom metrics API
Cloud Monitoring custom metrics API (option D) is the correct choice because it allows a developer to push custom application-specific metrics (e.g., request latency, queue depth) from a GKE pod using the `custom.googleapis.com` metric domain. This integrates directly with Cloud Monitoring for alerting and dashboards, whereas Cloud Logging is for log data, not 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.
- ✗
Cloud Logging
Why it's wrong here
Cloud Logging stores discrete log entries, so a custom metric value would need a log-based metric extraction rather than direct emission. It is tempting because logs can be queried and alert on, but it would be the right choice for capturing event text or structured payloads, not for recording a numeric measurement with Cloud Monitoring.
- ✗
Cloud Trace
Why it's wrong here
Cloud Trace records distributed request latency across services, so it captures span timing rather than arbitrary application-defined numeric values. It is tempting because it also instruments GKE workloads, but it would be the correct choice when diagnosing where latency accumulates in a request path, not for reporting a custom metric.
- ✗
Cloud Debugger
Why it's wrong here
Cloud Debugger inspects application state at breakpoints during live debugging sessions; it captures variable values and call stacks, not numeric time-series data. It is tempting because it targets running GKE workloads, but it would be the right choice for diagnosing a code-level defect, not for emitting a custom metric to Cloud Monitoring.
- ✓
Cloud Monitoring custom metrics API
Why this is correct
Cloud Monitoring's custom metrics API accepts user-defined time series from GKE workloads, satisfying the requirement to monitor an application-specific metric rather than built-in system telemetry. The developer writes metric descriptors and time-series data via the API, which Cloud Monitoring then charts and alerts on.
Go deeper
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Data Migration and Transfer Services
Key term
Cloud logging
Cloud logging is the practice of collecting, storing, and analyzing log data generated by cloud-based resources and applications to monitor performance, troubleshoot issues, and maintain security.
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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