- A
Monitor average latency because it is most representative of typical user experience.
Why wrong: Average latency can mask outliers and is not suitable for SLOs that require strict performance guarantees.
- B
Monitor container CPU utilization as a proxy for application latency.
Why wrong: CPU utilization is an infrastructure metric that does not directly measure user-facing latency; it is not appropriate for SLO definition.
- C
Monitor the 99th percentile of request latency directly using Cloud Monitoring custom metrics.
Direct latency measurement at the 99th percentile accurately reflects the experience of slow requests and is a standard SLO indicator.
- D
Use logs-based metrics to count error rates (e.g., HTTP 5xx responses).
Error rate is a key component of SLOs; logs-based metrics allow flexible and precise counting of errors from application logs.
- E
Use the number of running pods as the primary SLO indicator.
Why wrong: Pod count indicates availability but not performance; it is not a direct measure of service quality.
PCDOE Implementing service monitoring strategies Practice Question
This PCDOE practice question tests your understanding of implementing service monitoring strategies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, tail latency (e.g., p99) is critical because modern distributed systems exhibit high variability due to queuing, garbage collection, or network retransmissions; Google's SRE practices emphasize using histograms in Cloud Monitoring to track percentiles rather than averages. In a real-world scenario, a microservice might have average latency of 50ms but p99 of 2s due to a single slow database query, which would violate a strict SLO if only average was monitored.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PCDOE question test?
Implementing service monitoring strategies — This question tests Implementing service monitoring strategies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Monitor the 99th percentile of request latency directly using Cloud Monitoring custom metrics. — Option C is correct because 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.
What should I do if I get this PCDOE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 30, 2026
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.
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