Managing Implementation and Ensuring Solution and Operations Reliability →easyMultiple ChoiceObjective-mapped
Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
You are configuring a Cloud Monitoring alerting policy to notify your SRE team when the error rate of a service exceeds 5% over a 5-minute window. Which type of metric evaluation should you use?
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
✓
Window-based
A window-based metric evaluation (e.g., rate, ratio, or count over a sliding window) is appropriate for error rate over a 5-minute window. Request-based is for latency SLOs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Window-based
Why this is correct
Window-based metrics evaluate conditions over a sliding time window.
- ✗
Log-based
Why it's wrong here
Log-based alerts are for specific log entries, not metric thresholds.
- ✗
Request-based
Why it's wrong here
Request-based is for latency SLOs, not error rate over a window.
- ✗
Health check-based
Why it's wrong here
Health checks are for instance-level availability, not error rate.
Go deeper
Related to this question
Learn chapter
Introduction to Google Cloud Platform
Key term
Alerting policy
An alerting policy is a set of rules that defines when to send notifications about a system condition that needs attention.
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
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JA
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.