- A
Adjust resource requests to 1 Gi memory and 2 CPU, set limits to 2 Gi and 4 CPU, create an HPA based on a custom metric (e.g., requests per second), enable cluster autoscaler, implement Cloud SQL connection pooling via Cloud SQL Auth Proxy with a max connection pool size, and configure PDB with maxUnavailable 1.
Correctly addresses all issues: resource tuning for OOM, custom metric HPA for load, cluster autoscaler for capacity, connection pooling for timeouts, and PDB for maintenance.
- B
Enable GKE node auto-upgrade, configure Pod Disruption Budgets (PDB) with minAvailable 1, and set readiness probes to check application health.
Why wrong: Addresses maintenance but does not fix OOM errors, connection exhaustion, or scaling under load.
- C
Migrate the database to a StatefulSet in GKE with persistent volumes, increase node count to 10, and enable cluster autoscaler.
Why wrong: Running database on GKE adds complexity and does not solve connection pooling or OOM issues; scaling nodes alone is insufficient.
- D
Increase memory limits to 2 Gi and CPU to 2, add Horizontal Pod Autoscaler (HPA) based on CPU utilization, and implement connection pooling using Cloud SQL Auth Proxy.
Why wrong: Increases limits may reduce OOM but does not address readiness probes or node maintenance; CPU-based HPA may not respond to latency spikes.
Google PCA Ensure solution and operations reliability Practice Question
This PCA practice question tests your understanding of ensure solution and operations reliability. 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 financial services company is migrating a monolithic Java application to Google Kubernetes Engine (GKE) for improved scalability and reliability. The application serves real-time trading data and has strict latency requirements. Post-migration, the team observes frequent pod restarts due to OutOfMemory (OOM) errors, increased latency during peak trading hours, and occasional database connection timeouts. The current setup uses a single GKE cluster with a node pool of n1-standard-4 machines, a stateless application deployed as a Deployment with resource requests and limits set to 512 Mi memory and 1 CPU. The database is a Cloud SQL PostgreSQL instance with 2 vCPUs and 7.5 GB memory, and applications connect using a hardcoded connection string. The team wants to ensure reliable operation under load and during node maintenance events. Which course of action best addresses the reliability issues?
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
Adjust resource requests to 1 Gi memory and 2 CPU, set limits to 2 Gi and 4 CPU, create an HPA based on a custom metric (e.g., requests per second), enable cluster autoscaler, implement Cloud SQL connection pooling via Cloud SQL Auth Proxy with a max connection pool size, and configure PDB with maxUnavailable 1.
Option C comprehensively addresses all issues: setting resource requests ensures scheduling, limits prevent OOM, HPA on custom metrics (e.g., requests per second) scales based on load, Cloud SQL connection pooling with Cloud SQL Auth Proxy prevents connection exhaustion and adds security, cluster autoscaler handles node capacity, and PDB ensures availability during maintenance. Option A misses readiness probes and autoscaling; Option B ignores resource limits and connection pooling; Option D uses StatefulSet unnecessarily and omits connection pooling and HPA on custom metrics.
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.
- ✓
Adjust resource requests to 1 Gi memory and 2 CPU, set limits to 2 Gi and 4 CPU, create an HPA based on a custom metric (e.g., requests per second), enable cluster autoscaler, implement Cloud SQL connection pooling via Cloud SQL Auth Proxy with a max connection pool size, and configure PDB with maxUnavailable 1.
Why this is correct
Correctly addresses all issues: resource tuning for OOM, custom metric HPA for load, cluster autoscaler for capacity, connection pooling for timeouts, and PDB for maintenance.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable GKE node auto-upgrade, configure Pod Disruption Budgets (PDB) with minAvailable 1, and set readiness probes to check application health.
Why it's wrong here
Addresses maintenance but does not fix OOM errors, connection exhaustion, or scaling under load.
- ✗
Migrate the database to a StatefulSet in GKE with persistent volumes, increase node count to 10, and enable cluster autoscaler.
Why it's wrong here
Running database on GKE adds complexity and does not solve connection pooling or OOM issues; scaling nodes alone is insufficient.
- ✗
Increase memory limits to 2 Gi and CPU to 2, add Horizontal Pod Autoscaler (HPA) based on CPU utilization, and implement connection pooling using Cloud SQL Auth Proxy.
Why it's wrong here
Increases limits may reduce OOM but does not address readiness probes or node maintenance; CPU-based HPA may not respond to latency spikes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which PCA exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PCA question test?
Ensure solution and operations reliability — This question tests Ensure solution and operations reliability — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Adjust resource requests to 1 Gi memory and 2 CPU, set limits to 2 Gi and 4 CPU, create an HPA based on a custom metric (e.g., requests per second), enable cluster autoscaler, implement Cloud SQL connection pooling via Cloud SQL Auth Proxy with a max connection pool size, and configure PDB with maxUnavailable 1. — Option C comprehensively addresses all issues: setting resource requests ensures scheduling, limits prevent OOM, HPA on custom metrics (e.g., requests per second) scales based on load, Cloud SQL connection pooling with Cloud SQL Auth Proxy prevents connection exhaustion and adds security, cluster autoscaler handles node capacity, and PDB ensures availability during maintenance. Option A misses readiness probes and autoscaling; Option B ignores resource limits and connection pooling; Option D uses StatefulSet unnecessarily and omits connection pooling and HPA on custom metrics.
What should I do if I get this PCA question wrong?
Identify which PCA exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 24, 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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