- A
Add a read replica for Cloud SQL to offload read queries.
Why wrong: Database latency is low, so a read replica would not improve performance and would incur additional cost.
- B
Migrate the backend service to a custom machine type with more vCPUs.
Why wrong: The backend is not the bottleneck; upgrading its resources would be expensive and unlikely to improve overall latency.
- C
Enable vertical pod autoscaling for the backend service.
Why wrong: Backend CPU is only 50% and not the bottleneck; vertical scaling could help but is less impactful and may increase cost without addressing the frontend.
- D
Increase the number of frontend pods by adjusting the horizontal pod autoscaler's target CPU utilization.
Frontend CPU is high, so scaling out frontend pods will help handle the load and reduce latency. This is cost-effective as it adds only needed capacity.
Google PCA Design and plan a cloud solution architecture Practice Question
This PCA practice question tests your understanding of design and plan a cloud solution architecture. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 runs a multi-tier web application on Google Kubernetes Engine (GKE) with a frontend service, a backend service, and a Cloud SQL for PostgreSQL database. During peak hours, the frontend pod CPU usage is high (consistently above 80%), while the backend service shows moderate CPU usage (around 50%). Response times for user requests increase significantly, often exceeding the 200ms p99 latency target. Cloud SQL metrics show low query latency and no contention. The team wants to improve performance in a cost-effective manner. Which initial step should they take?
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
Increase the number of frontend pods by adjusting the horizontal pod autoscaler's target CPU utilization.
The frontend pods are CPU-bound during peak hours, causing increased response times. Increasing the number of frontend pods via the Horizontal Pod Autoscaler (HPA) by lowering the target CPU utilization threshold distributes the load across more replicas, directly addressing the bottleneck without additional infrastructure cost. This is the most cost-effective initial step because it leverages existing resources and autoscaling capabilities.
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.
- ✗
Add a read replica for Cloud SQL to offload read queries.
Why it's wrong here
Database latency is low, so a read replica would not improve performance and would incur additional cost.
- ✗
Migrate the backend service to a custom machine type with more vCPUs.
Why it's wrong here
The backend is not the bottleneck; upgrading its resources would be expensive and unlikely to improve overall latency.
- ✗
Enable vertical pod autoscaling for the backend service.
Why it's wrong here
Backend CPU is only 50% and not the bottleneck; vertical scaling could help but is less impactful and may increase cost without addressing the frontend.
- ✓
Increase the number of frontend pods by adjusting the horizontal pod autoscaler's target CPU utilization.
Why this is correct
Frontend CPU is high, so scaling out frontend pods will help handle the load and reduce latency. This is cost-effective as it adds only needed capacity.
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 misconception that backend or database changes are needed when the bottleneck is clearly at the frontend tier, leading candidates to choose expensive or irrelevant scaling options like read replicas or vertical scaling.
Detailed technical explanation
How to think about this question
The Horizontal Pod Autoscaler (HPA) in GKE uses the Kubernetes Metrics Server to collect CPU utilization metrics and adjusts the number of pod replicas based on the target utilization percentage. By default, HPA uses a target of 80% CPU, but lowering it to, say, 60% triggers earlier scaling, reducing per-pod load and improving response times. This approach is cost-effective because it uses spot or preemptible nodes for the frontend tier, and the stateless nature of frontend pods allows seamless horizontal scaling without data consistency concerns.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this PCA question test?
Design and plan a cloud solution architecture — This question tests Design and plan a cloud solution architecture — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of frontend pods by adjusting the horizontal pod autoscaler's target CPU utilization. — The frontend pods are CPU-bound during peak hours, causing increased response times. Increasing the number of frontend pods via the Horizontal Pod Autoscaler (HPA) by lowering the target CPU utilization threshold distributes the load across more replicas, directly addressing the bottleneck without additional infrastructure cost. This is the most cost-effective initial step because it leverages existing resources and autoscaling capabilities.
What should I do if I get this PCA 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 PCA 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 PCA exam.
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