The answer is to increase the CPU and memory of the Cloud SQL instance to handle the load. This is correct because the primary bottleneck is the database, not the application layer: Cloud SQL is pegged at 90% CPU with high query latency, while the backend service on GKE still has headroom at 60% CPU and normal memory. When diagnosing a slow GKE application, you must trace latency to its source—here, Cloud Trace confirms the backend is slow only on certain requests, and those requests trigger repeated database queries that overwhelm the database during peak hours. On the Google Professional Cloud Developer exam, this scenario tests your ability to differentiate between scaling the compute layer versus the data layer; a common trap is to scale the GKE pods first, which would only increase database contention. Remember the “bottleneck rule”: always scale the resource that is saturated—in this case, the database CPU. Memory tip: “If the DB is screaming, scaling pods is just dreaming.”
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 company runs a multi-tier web application on Google Kubernetes Engine (GKE). The application consists of a frontend service, a backend API service, and a PostgreSQL database managed by Cloud SQL. Recently, users have been reporting intermittent slow response times during peak hours (10 AM - 12 PM). You have set up Cloud Monitoring dashboards and alerts. Cloud Trace shows that the backend API service has high latency, but only for certain requests. You notice that the backend service's CPU utilization is around 60% during peak hours, and memory usage is normal. The Cloud SQL instance's CPU utilization is at 90% and the query latency is high. You have also observed that the backend service makes multiple database queries per request, some of which are repeated. What is the most effective course of action to reduce latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the CPU and memory of the Cloud SQL instance to handle the load
The primary bottleneck is the Cloud SQL instance, which is running at 90% CPU with high query latency. Since the backend service's CPU is only at 60% and memory is normal, scaling the database directly addresses the root cause. Increasing the Cloud SQL instance's CPU and memory provides more processing power and connection capacity to handle the peak load, reducing query latency and overall response times.
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.
✓
Increase the CPU and memory of the Cloud SQL instance to handle the load
Why this is correct
The database is at 90% CPU, so increasing its resources directly reduces query latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Scale up the backend API service by increasing the number of replicas
Why it's wrong here
Backend CPU is only 60%, and the bottleneck is the database.
✗
Scale up the frontend service by increasing the number of replicas
Why it's wrong here
The frontend is not the bottleneck; the database is.
✗
Implement a caching layer using Memorystore for Redis to cache database query results
Why it's wrong here
Caching can help, but it is a longer-term optimization; the immediate issue is database CPU.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling application replicas (horizontal scaling) always improves performance, but here the bottleneck is the database, not the application, so vertical scaling of the database is required.
Detailed technical explanation
How to think about this question
Cloud SQL instances have fixed vCPU and memory allocations; at 90% CPU, the instance is likely experiencing resource contention and connection queuing, which increases query execution time. Increasing the instance tier (e.g., from db-n1-standard-2 to db-n1-standard-4) doubles the vCPU count and memory, directly improving throughput for concurrent queries. Additionally, Cloud SQL's automatic storage increase does not address CPU saturation, making vertical scaling the immediate fix.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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: Increase the CPU and memory of the Cloud SQL instance to handle the load — The primary bottleneck is the Cloud SQL instance, which is running at 90% CPU with high query latency. Since the backend service's CPU is only at 60% and memory is normal, scaling the database directly addresses the root cause. Increasing the Cloud SQL instance's CPU and memory provides more processing power and connection capacity to handle the peak load, reducing query latency and overall response times.
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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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.
Question Discussion
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