- A
Use a single cluster per region.
Why wrong: Multiple clusters per region may be needed for higher availability.
- B
Use a single replica for each service to reduce cost.
Why wrong: Single replica is a single point of failure.
- C
Use horizontal pod autoscaling based on custom metrics.
HPA allows scaling based on application-specific metrics.
- D
Use stateful sets for all services.
Why wrong: StatefulSets are only needed for stateful applications.
- E
Deploy services across multiple zones.
Multi-zone deployment protects against zone failures.
PCD Practice Question: Designing highly scalable, available, and reliable cloud-native applications
This PCD practice question tests your understanding of designing highly scalable, available, and reliable cloud-native applications. 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 company is designing a cloud-native application on Google Kubernetes Engine. They want to ensure high availability and scalability for their microservices. Which two best practices should they follow?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Use horizontal pod autoscaling based on custom metrics.
Horizontal Pod Autoscaling (HPA) based on custom metrics allows the application to automatically scale the number of pod replicas in response to application-specific signals (e.g., requests per second, queue depth) rather than just CPU/memory. This ensures that each microservice can handle varying load efficiently, maintaining high availability and scalability without over-provisioning.
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.
- ✗
Use a single cluster per region.
Why it's wrong here
Multiple clusters per region may be needed for higher availability.
- ✗
Use a single replica for each service to reduce cost.
Why it's wrong here
Single replica is a single point of failure.
- ✓
Use horizontal pod autoscaling based on custom metrics.
Why this is correct
HPA allows scaling based on application-specific metrics.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use stateful sets for all services.
Why it's wrong here
StatefulSets are only needed for stateful applications.
- ✓
Deploy services across multiple zones.
Why this is correct
Multi-zone deployment protects against zone failures.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that high availability requires a single cluster per region, but the trap is that true resilience demands multi-zone or multi-region deployment to survive zonal failures, not just cluster redundancy.
Detailed technical explanation
How to think about this question
HPA with custom metrics relies on the Kubernetes Metrics Server and the custom.metrics.k8s.io API, which can be backed by Prometheus or other monitoring systems. In a multi-zone GKE cluster, deploying services across zones ensures that if an entire zone fails, the remaining zones continue serving traffic, leveraging GKE's regional persistent disks and zonal managed instance groups for resilience. Real-world scenarios like Black Friday traffic spikes demonstrate that HPA with custom metrics (e.g., queue length) prevents thundering herd problems better than CPU-based autoscaling.
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
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.
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Designing highly scalable, available, and reliable cloud-native applications — study guide chapter
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FAQ
Questions learners often ask
What does this PCD question test?
Designing highly scalable, available, and reliable cloud-native applications — This question tests Designing highly scalable, available, and reliable cloud-native applications — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use horizontal pod autoscaling based on custom metrics. — Horizontal Pod Autoscaling (HPA) based on custom metrics allows the application to automatically scale the number of pod replicas in response to application-specific signals (e.g., requests per second, queue depth) rather than just CPU/memory. This ensures that each microservice can handle varying load efficiently, maintaining high availability and scalability without over-provisioning.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 25, 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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