- A
Set up a log-based metric for 5xx responses and create an alert on the metric.
Why wrong: Log-based metrics have ingestion latency, often exceeding 1 minute, and may not meet the sub-minute alert requirement.
- B
Create a Cloud Logging sink to a Pub/Sub topic and trigger a Cloud Function that sends notifications.
Why wrong: This approach relies on Cloud Logging's export latency, which can be several minutes, and does not provide a rate-based condition.
- C
Use Cloud Monitoring's log-based alerting to trigger on every 5xx log entry.
Why wrong: Alerting on every log entry can cause alert fatigue and does not allow for a rate condition like exceeding 1%.
- D
Create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window.
Cloud Monitoring supports metric evaluation every few seconds, and the ratio condition meets the requirement of alerting within 1 minute.
Quick Answer
The correct approach is to create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window. This works because Cloud Monitoring’s Metrics Explorer can compute a ratio directly from the same metric's two labels—response_code_class and response_code_count—allowing you to define a custom alert condition that evaluates the percentage of 5xx errors against all requests within a sliding 1-minute window, meeting the strict one-minute notification latency without needing additional logging or infrastructure. On the Google Professional Cloud DevOps Engineer exam, this tests your understanding of Cloud Monitoring’s built-in metric filtering and ratio-based alerting, a common trap is assuming you must export logs to create a log-based metric first, which would introduce unnecessary delay. Remember the memory tip: “Ratio on the metric, not on the log” to avoid overcomplicating the alert setup.
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 company uses Cloud Run for a critical service and needs to set up alerting for 5xx errors. They want to receive a notification within 1 minute of the error rate exceeding 1% for any 1-minute window. Which alerting approach should they 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
Create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window.
Option D is correct because Cloud Monitoring's alerting policies can directly compute the ratio of 5xx responses to total requests using the 'Request count' metric with a custom ratio condition over a 1-minute window. This approach meets the 1-minute notification latency requirement without additional infrastructure, as Cloud Monitoring evaluates the metric every 60 seconds and triggers alerts based on the sliding window.
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.
- ✗
Set up a log-based metric for 5xx responses and create an alert on the metric.
Why it's wrong here
Log-based metrics have ingestion latency, often exceeding 1 minute, and may not meet the sub-minute alert requirement.
- ✗
Create a Cloud Logging sink to a Pub/Sub topic and trigger a Cloud Function that sends notifications.
Why it's wrong here
This approach relies on Cloud Logging's export latency, which can be several minutes, and does not provide a rate-based condition.
- ✗
Use Cloud Monitoring's log-based alerting to trigger on every 5xx log entry.
Why it's wrong here
Alerting on every log entry can cause alert fatigue and does not allow for a rate condition like exceeding 1%.
- ✓
Create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window.
Why this is correct
Cloud Monitoring supports metric evaluation every few seconds, and the ratio condition meets the requirement of alerting within 1 minute.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between log-based metrics (which have inherent sampling and aggregation delays) and built-in request metrics (which are real-time and window-aware), leading candidates to mistakenly choose log-based alerting for rate-based conditions.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Monitoring's 'Request count' metric is a built-in metric from Cloud Run's telemetry, collected every 60 seconds with sub-minute latency, allowing alerting policies to evaluate sliding window conditions like 'ratio of 5xx responses to total requests > 0.01' using MQL (Monitoring Query Language). In a real-world scenario, if a deployment introduces a bug causing intermittent 5xx errors, this alerting policy catches the spike within the same minute, whereas log-based methods would miss the window due to aggregation delays.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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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: Create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window. — Option D is correct because Cloud Monitoring's alerting policies can directly compute the ratio of 5xx responses to total requests using the 'Request count' metric with a custom ratio condition over a 1-minute window. This approach meets the 1-minute notification latency requirement without additional infrastructure, as Cloud Monitoring evaluates the metric every 60 seconds and triggers alerts based on the sliding window.
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.
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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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