- A
Deploy the entire application in a single container with a large custom machine type to handle load.
Why wrong: This does not improve scalability or reliability; it creates a single point of failure.
- B
Refactor the application into microservices and deploy each as a separate deployment in GKE.
Microservices allow independent scaling and faster deployments.
- C
Expose the application using a simple Service of type LoadBalancer with round-robin distribution.
Why wrong: A basic LoadBalancer may not handle session persistence or health checks adequately.
- D
Use Cloud SQL for MySQL instead of running the database in the same cluster.
Managed databases provide high availability and reduce operational burden.
- E
Use a single Pod with multiple containers that communicate via localhost to reduce latency.
Why wrong: This approach does not improve scalability; containers in the same Pod cannot scale independently.
Google PCA Manage implementation of cloud architecture Practice Question
This PCA practice question tests your understanding of manage implementation of cloud architecture. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 organization is moving a legacy monolithic application to Google Kubernetes Engine (GKE). The application currently runs on a single virtual machine with a local MySQL database. You need to design a cloud-native architecture that improves scalability and reliability. Which two actions should you take? (Choose TWO.)
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
Refactor the application into microservices and deploy each as a separate deployment in GKE.
Option B is correct because refactoring the monolithic application into microservices and deploying each as a separate Deployment in GKE aligns with cloud-native principles, enabling independent scaling, fault isolation, and easier updates. This approach improves scalability and reliability by allowing each microservice to scale horizontally based on demand, and failures in one service do not cascade to others.
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.
- ✗
Deploy the entire application in a single container with a large custom machine type to handle load.
Why it's wrong here
This does not improve scalability or reliability; it creates a single point of failure.
- ✓
Refactor the application into microservices and deploy each as a separate deployment in GKE.
Why this is correct
Microservices allow independent scaling and faster deployments.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Expose the application using a simple Service of type LoadBalancer with round-robin distribution.
Why it's wrong here
A basic LoadBalancer may not handle session persistence or health checks adequately.
- ✓
Use Cloud SQL for MySQL instead of running the database in the same cluster.
Why this is correct
Managed databases provide high availability and reduce operational burden.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a single Pod with multiple containers that communicate via localhost to reduce latency.
Why it's wrong here
This approach does not improve scalability; containers in the same Pod cannot scale independently.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that simply containerizing a monolith or using a larger machine type is sufficient for cloud-native scalability, when in fact true scalability requires decoupling components into independently scalable units and separating stateful services like databases.
Detailed technical explanation
How to think about this question
Under the hood, GKE Deployments manage ReplicaSets to ensure a desired number of Pod replicas are running, and HorizontalPodAutoscaler can automatically scale replicas based on CPU, memory, or custom metrics. Using Cloud SQL for MySQL (Option D) offloads database management, provides automated backups, replication, and failover, and decouples state from compute, which is critical for stateless microservices to scale independently without data loss 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
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 PCA question test?
Manage implementation of cloud architecture — This question tests Manage implementation of cloud architecture — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Refactor the application into microservices and deploy each as a separate deployment in GKE. — Option B is correct because refactoring the monolithic application into microservices and deploying each as a separate Deployment in GKE aligns with cloud-native principles, enabling independent scaling, fault isolation, and easier updates. This approach improves scalability and reliability by allowing each microservice to scale horizontally based on demand, and failures in one service do not cascade to others.
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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