- A
Set the alert to fire only if the condition persists for a longer duration.
Why wrong: While duration can reduce transient spikes, a single slow request that sustains for a minute could still trigger a false alarm; shifting percentile is more effective.
- B
Use a log-based metric instead of latency.
Why wrong: A log-based metric counts logs, not latency; it would not help identify slow requests.
- C
Increase the alerting threshold to 2 seconds.
Why wrong: Raising the threshold may mask real performance issues and does not address the root cause of outlier sensitivity; it's not a robust solution.
- D
Use a different latency metric like median or 95th percentile.
Lower percentiles are less sensitive to outliers, reducing false alarms while still capturing most user experience.
PCD Managing application performance monitoring Practice Question
This PCD practice question tests your understanding of managing application performance monitoring. 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.
Your team manages a service that receives thousands of requests per second. They have set up Cloud Monitoring alerting based on the 99th percentile latency. Recently, they received an alert warning that latency exceeded 1 second, but after investigating, they found it was a false alarm caused by a single very slow request. How can they improve their alert to reduce false positives?
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
Use a different latency metric like median or 95th percentile.
Option D is correct because using a different latency metric like the 95th percentile reduces sensitivity to outliers. The 99th percentile captures the slowest 1% of requests, so a single very slow request can trigger an alert. The 95th percentile ignores the top 5% of outliers, making it more robust against isolated slow requests while still monitoring tail latency effectively.
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 the alert to fire only if the condition persists for a longer duration.
Why it's wrong here
While duration can reduce transient spikes, a single slow request that sustains for a minute could still trigger a false alarm; shifting percentile is more effective.
- ✗
Use a log-based metric instead of latency.
Why it's wrong here
A log-based metric counts logs, not latency; it would not help identify slow requests.
- ✗
Increase the alerting threshold to 2 seconds.
Why it's wrong here
Raising the threshold may mask real performance issues and does not address the root cause of outlier sensitivity; it's not a robust solution.
- ✓
Use a different latency metric like median or 95th percentile.
Why this is correct
Lower percentiles are less sensitive to outliers, reducing false alarms while still capturing most user experience.
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 exams often test the misconception that increasing thresholds or durations is the best way to reduce false positives, when the real solution is to adjust the statistical metric to be less sensitive to outliers.
Detailed technical explanation
How to think about this question
Percentile-based latency metrics (e.g., p99, p95) are calculated from a histogram of request durations over a time window. The p99 is sensitive to the top 1% of values, so a single outlier can skew it if the sample size is small relative to the window. Using p95 or median (p50) smooths out such spikes because they ignore a larger fraction of extreme values. In practice, many monitoring systems like Google Cloud Monitoring allow you to configure custom percentile thresholds, and choosing p95 is a common trade-off between outlier sensitivity and alert accuracy.
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 PCD question test?
Managing application performance monitoring — This question tests Managing application performance monitoring — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a different latency metric like median or 95th percentile. — Option D is correct because using a different latency metric like the 95th percentile reduces sensitivity to outliers. The 99th percentile captures the slowest 1% of requests, so a single very slow request can trigger an alert. The 95th percentile ignores the top 5% of outliers, making it more robust against isolated slow requests while still monitoring tail latency effectively.
What should I do if I get this PCD 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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
This PCD 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 PCD exam.
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