PCDOE Implementing service monitoring strategies Practice Question
A site reliability engineer is defining SLOs for a microservice application running on Google Kubernetes Engine. The application serves user-facing API requests. Which TWO approaches should the engineer take to effectively monitor the service's performance?
⚠ Common exam trap
Google Cloud often tests the misconception that average latency or infrastructure metrics like CPU/pod count are sufficient for SLOs, when in fact user-facing SLOs must directly measure the user experience via tail latency and error rates.
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
✓
Monitor the 99th percentile of request latency directly using Cloud Monitoring custom metrics.
Monitoring the 99th percentile (p99) of request latency directly captures the experience of the slowest 1% of users, which is critical for user-facing APIs where tail latency directly impacts user satisfaction. Cloud Monitoring custom metrics allow the engineer to instrument the application to emit precise latency distributions, enabling accurate SLO tracking rather than relying on averages that mask outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Monitor average latency because it is most representative of typical user experience.
Why it's wrong here
Average latency can mask outliers and is not suitable for SLOs that require strict performance guarantees.
- ✗
Monitor container CPU utilization as a proxy for application latency.
Why it's wrong here
CPU utilization is an infrastructure metric that does not directly measure user-facing latency; it is not appropriate for SLO definition.
- ✓
Monitor the 99th percentile of request latency directly using Cloud Monitoring custom metrics.
Why this is correct
Direct latency measurement at the 99th percentile accurately reflects the experience of slow requests and is a standard SLO indicator.
- ✓
Use logs-based metrics to count error rates (e.g., HTTP 5xx responses).
Why this is correct
Error rate is a key component of SLOs; logs-based metrics allow flexible and precise counting of errors from application logs.
- ✗
Use the number of running pods as the primary SLO indicator.
Why it's wrong here
Pod count indicates availability but not performance; it is not a direct measure of service quality.
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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.