Google PCA Ensure solution and operations reliability Practice Question
Your company runs a critical application on Google Kubernetes Engine (GKE) with 5 nodes. The application experiences intermittent high latency every Friday afternoon. The team has ruled out infrastructure issues and suspects the application logic. You need to instrument the application to identify the root cause. Which approach should you take?
⚠ Common exam trap
A common mix-up: candidates confuse operational logging (Option C) with performance monitoring, failing to recognize that intermittent latency without errors requires custom metrics to measure application-specific performance indicators.
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
✓
Use Cloud Monitoring to create custom metrics for application performance and investigate recent code changes.
The team has already ruled out infrastructure issues and suspects application logic. Creating custom metrics in Cloud Monitoring allows you to instrument the application with key performance indicators (e.g., request latency, error rates) and correlate them with recent code changes to pinpoint the root cause of intermittent high latency. This approach directly addresses the need to monitor application-level behavior rather than infrastructure 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.
- ✓
Use Cloud Monitoring to create custom metrics for application performance and investigate recent code changes.
Why this is correct
Cloud Monitoring custom metrics expose application-level performance signals, letting the team correlate Friday latency spikes with recent code changes rather than infrastructure. This instruments application logic directly, satisfying the need to pinpoint the root cause after infrastructure was ruled out.
- ✗
Increase the number of nodes in the GKE cluster to handle the load.
Why it's wrong here
Adding nodes addresses capacity, not application logic; the stem already rules out infrastructure and asks for instrumentation. Horizontal scaling would be correct for genuine resource saturation, but here it merely masks the Friday latency without revealing which code path stalls.
- ✗
Enable Cloud Logging and analyze logs for error messages during the latency periods.
Why it's wrong here
Log analysis only surfaces errors already emitted; intermittent latency from slow code paths may produce no error entries, so root cause stays hidden. Cloud Logging suits post-incident forensics on explicit failures, whereas the stem demands instrumenting the application itself to trace execution.
- ✗
Configure GKE usage metering to track resource consumption by namespace.
Why it's wrong here
Usage metering reports namespace-level CPU and memory consumption, not per-request traces or code-level timing, so it cannot expose the faulty application logic. It suits cost allocation and capacity reporting across tenants, not diagnosing intermittent latency inside a single workload.
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PCA practice question is part of Courseiva's free Google Cloud 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 PCA exam.