- A
Add more nodes to the GKE cluster and enable cluster autoscaling
Why wrong: Adding nodes without increasing pods doesn't reduce CPU usage per node; connection pool issue remains.
- B
Increase the number of pod replicas and configure a connection pooler like PgBouncer for Cloud SQL
More pods distribute CPU load, and a connection pooler reduces database connections, addressing both issues cost-effectively.
- C
Vertically scale the GKE node pool to larger machine types and increase Cloud SQL tier
Why wrong: Vertical scaling is costly and may not solve connection pool exhaustion; also architectural change is minimal but not optimal.
- D
Set up a Cloud SQL read replica and route read queries to it
Why wrong: Read replica does not reduce write connection pool exhaustion; also adds complexity and cost.
Quick Answer
The correct answer is to increase the number of pod replicas and add a connection pooler like PgBouncer for Cloud SQL. This solves the dual problem of high CPU on GKE nodes and database connection pool exhaustion because the application is stateless—scaling out replicas distributes the CPU load horizontally, while PgBouncer multiplexes many application connections into a smaller pool of persistent database connections, preventing Cloud SQL from hitting its max connection limit. On the Google Professional Cloud Architect exam, this scenario tests your ability to decouple compute scaling from database scaling; a common trap is to immediately suggest a larger Cloud SQL machine type, which is costly and doesn’t address the root cause of connection churn. Remember the memory tip: “Scale out the pods, pool the connections”—this keeps your architecture lean and your database stable without over-provisioning.
Google PCA Practice Question: Analyze and optimize technical and business processes
This PCA practice question tests your understanding of analyze and optimize technical and business processes. 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 startup runs a web application on Google Kubernetes Engine (GKE) with 3 replicas serving user traffic. They use Cloud SQL for the database. Recently, the application experienced intermittent timeouts during peak hours. Monitoring shows high CPU usage on the GKE nodes and increased database connection pool exhaustion. The team is looking for a cost-effective solution that minimizes architectural changes. The application is stateless. What should they do?
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 pod replicas and configure a connection pooler like PgBouncer for Cloud SQL
The application is stateless and experiencing database connection pool exhaustion alongside high CPU on GKE nodes. Increasing pod replicas distributes the CPU load across more pods, while adding a connection pooler like PgBouncer reduces the number of direct connections to Cloud SQL, preventing pool exhaustion without requiring database tier changes. This approach is cost-effective because it optimizes existing resources rather than scaling infrastructure.
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 more nodes to the GKE cluster and enable cluster autoscaling
Why it's wrong here
Adding nodes without increasing pods doesn't reduce CPU usage per node; connection pool issue remains.
- ✓
Increase the number of pod replicas and configure a connection pooler like PgBouncer for Cloud SQL
Why this is correct
More pods distribute CPU load, and a connection pooler reduces database connections, addressing both issues cost-effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Vertically scale the GKE node pool to larger machine types and increase Cloud SQL tier
Why it's wrong here
Vertical scaling is costly and may not solve connection pool exhaustion; also architectural change is minimal but not optimal.
- ✗
Set up a Cloud SQL read replica and route read queries to it
Why it's wrong here
Read replica does not reduce write connection pool exhaustion; also adds complexity and cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling compute resources (nodes or pods) alone fixes database connection issues, but the trap here is that connection pool exhaustion is a database-layer problem requiring a connection pooler, not just more application instances.
Detailed technical explanation
How to think about this question
PgBouncer acts as a lightweight connection pooler that maintains a small number of persistent connections to Cloud SQL and multiplexes them across many client connections, drastically reducing the number of concurrent connections hitting the database. In GKE, PgBouncer can be deployed as a sidecar container or a separate service, and it supports transaction pooling (session pooling is default) which is ideal for stateless applications. Cloud SQL has a default max_connections limit (e.g., 100 for db-f1-micro, up to 4000 for high-memory tiers), and connection pool exhaustion occurs when pods open too many connections simultaneously, overwhelming that limit.
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?
Analyze and optimize technical and business processes — This question tests Analyze and optimize technical and business processes — 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 pod replicas and configure a connection pooler like PgBouncer for Cloud SQL — The application is stateless and experiencing database connection pool exhaustion alongside high CPU on GKE nodes. Increasing pod replicas distributes the CPU load across more pods, while adding a connection pooler like PgBouncer reduces the number of direct connections to Cloud SQL, preventing pool exhaustion without requiring database tier changes. This approach is cost-effective because it optimizes existing resources rather than scaling infrastructure.
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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