PCDOE Implementing service monitoring strategies Practice Question
You are designing a monitoring strategy for a microservices application running on Google Kubernetes Engine (GKE). You need to create a custom metric that counts the number of failed login attempts from the application logs. The logs are in JSON format and contain a field 'status' with value 'FAILED'. Which approach should you use?
⚠ Common exam trap
Google Cloud often tests the misconception that Metrics Explorer can create metrics from logs, when in fact it only queries and charts existing metrics, while logs-based metrics are the correct service for deriving custom metrics from log 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
✓
Configure a logs-based metric in Cloud Logging that filters for the condition and counts.
A logs-based metric in Cloud Logging directly counts occurrences of a specific log entry pattern (e.g., 'status' = 'FAILED') without requiring additional agents or data exports. This approach is purpose-built for deriving metrics from log data and integrates seamlessly with Cloud Monitoring for alerting and dashboards.
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's Metrics Explorer to create a metric from logs using a filter.
Why it's wrong here
Metrics Explorer visualizes existing metrics but cannot create new metrics from logs.
- ✗
Install the Ops Agent on GKE nodes to collect application metrics directly.
Why it's wrong here
The Ops Agent collects system and application metrics from VMs, not from logs; it cannot parse log content to count events.
- ✓
Configure a logs-based metric in Cloud Logging that filters for the condition and counts.
Why this is correct
Logs-based metrics are designed for this purpose – they count log entries that match a filter and expose them as custom metrics in Cloud Monitoring.
- ✗
Export logs to BigQuery and then create a custom metric from the exported data.
Why it's wrong here
Exporting logs to BigQuery introduces latency and additional infrastructure management, whereas the requirement demands real-time metric creation from GKE logs. This approach is tempting because BigQuery excels at ad-hoc analysis of large historical datasets, making it the correct choice for retrospective security audits or long-term trend analysis rather than live monitoring of authentication failures.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PCDOE question from scratch — 486 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PCDOE 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 PCDOE exam.