- A
Check the network latency between the frontend and backend services using Cloud Monitoring's network metrics.
Why wrong: Since backend CPU is high, network latency is unlikely the primary issue; the bottleneck appears to be within the backend service.
- B
Analyze the backend service's request latency distribution using Cloud Monitoring metrics to identify whether the issue is due to increased request volume or slow request processing.
This directly addresses the symptom (backend CPU high) and helps determine if scaling or code optimization is needed.
- C
Configure the backend service's HPA to use custom metrics based on request latency instead of CPU utilization.
Why wrong: While latency-based scaling might help, changing the HPA metric without first diagnosing the root cause could mask underlying issues.
- D
Increase the minimum number of replicas for the backend service to handle peak traffic.
Why wrong: This is a workaround but does not address why the HPA is not scaling quickly enough; it may also lead to over-provisioning.
Quick Answer
The answer is to analyze the backend service's request latency distribution using Cloud Monitoring metrics. This is correct because when you diagnose intermittent slow GKE backend CPU high, the key clue is that the backend CPU spikes while the frontend CPU stays low and the database latency is normal—this isolates the bottleneck to request processing within the backend itself, not network or database issues. By examining the 99th percentile latency distribution, you can determine whether the slowdown is caused by a surge in request volume (shifting the entire distribution) or by individual requests taking longer to process (indicating code inefficiency or resource contention). On the Google Professional Cloud Developer exam, this scenario tests your ability to use observability tools to pinpoint performance degradation before tuning autoscaling or code; a common trap is jumping to adjust the HPA thresholds without first confirming the root cause. Memory tip: "CPU high, DB fine? Check the 99th line."
PCD Managing application performance monitoring Practice Question
This PCD practice question tests your understanding of managing application performance monitoring. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are managing a microservices application deployed on Google Kubernetes Engine (GKE) that uses Cloud Monitoring and Cloud Logging. Recently, users have reported intermittent slow response times, especially during peak hours. You have enabled the Ops Agent on GKE nodes and configured custom metrics for your services. The application consists of a frontend service, a backend API service, and a database service. The frontend calls the backend, which in turn queries the database. You notice that when the response time spikes, the frontend service's CPU utilization remains low, but the backend service's CPU utilization increases. The database service shows normal latency and no errors. You have examined the logs and found no application errors. The GKE cluster has three node pools: one for each service, with autoscaling enabled. The backend service is configured with a HorizontalPodAutoscaler (HPA) based on CPU utilization, but the HPA does not seem to scale up quickly enough during traffic spikes. You want to identify the root cause of the performance degradation. Which course of action should you take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Analyze the backend service's request latency distribution using Cloud Monitoring metrics to identify whether the issue is due to increased request volume or slow request processing.
Option B is correct because the intermittent slow response times during peak hours, combined with low frontend CPU but high backend CPU and normal database latency, strongly suggest the backend service is struggling to process requests quickly under load. Analyzing the backend's request latency distribution using Cloud Monitoring metrics (e.g., 99th percentile latency) will reveal whether the issue stems from increased request volume (which would show a shift in latency distribution) or from individual requests taking longer to process (e.g., due to inefficient code or resource contention). This diagnostic step directly addresses the symptom without making assumptions about scaling or network issues.
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.
- ✗
Check the network latency between the frontend and backend services using Cloud Monitoring's network metrics.
Why it's wrong here
Since backend CPU is high, network latency is unlikely the primary issue; the bottleneck appears to be within the backend service.
- ✓
Analyze the backend service's request latency distribution using Cloud Monitoring metrics to identify whether the issue is due to increased request volume or slow request processing.
Why this is correct
This directly addresses the symptom (backend CPU high) and helps determine if scaling or code optimization is needed.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure the backend service's HPA to use custom metrics based on request latency instead of CPU utilization.
Why it's wrong here
While latency-based scaling might help, changing the HPA metric without first diagnosing the root cause could mask underlying issues.
- ✗
Increase the minimum number of replicas for the backend service to handle peak traffic.
Why it's wrong here
This is a workaround but does not address why the HPA is not scaling quickly enough; it may also lead to over-provisioning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between symptom analysis and solution implementation, where candidates jump to scaling or metric changes (options C or D) without first performing a proper diagnostic step like analyzing latency distributions.
Detailed technical explanation
How to think about this question
The HorizontalPodAutoscaler (HPA) in GKE uses the Kubernetes Metrics Server to collect CPU utilization metrics every 15 seconds, but scaling decisions are made based on a sliding window of metrics, which can introduce latency of up to several minutes during traffic spikes. Custom metrics, such as request latency, can be exposed via the custom.metrics.k8s.io API using Prometheus Adapter or Stackdriver Adapter, but they require careful tuning of target values and stabilization windows to avoid thrashing. In this scenario, the backend's CPU increase suggests it is working harder, but without latency distribution data, you cannot distinguish between a surge in request volume (which HPA should handle) and a single slow request blocking others (which HPA may not help).
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
Got this wrong? Here's your next step.
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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: Analyze the backend service's request latency distribution using Cloud Monitoring metrics to identify whether the issue is due to increased request volume or slow request processing. — Option B is correct because the intermittent slow response times during peak hours, combined with low frontend CPU but high backend CPU and normal database latency, strongly suggest the backend service is struggling to process requests quickly under load. Analyzing the backend's request latency distribution using Cloud Monitoring metrics (e.g., 99th percentile latency) will reveal whether the issue stems from increased request volume (which would show a shift in latency distribution) or from individual requests taking longer to process (e.g., due to inefficient code or resource contention). This diagnostic step directly addresses the symptom without making assumptions about scaling or network issues.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 11, 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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