Google PCA Practice Question: Analyze and optimize technical and business processes
A company runs a high-traffic web application on Google Kubernetes Engine (GKE). The application uses a Cloud SQL for MySQL instance as its backend. The operations team wants to optimize the cost of the GKE cluster and the Cloud SQL instance without sacrificing performance or availability. Which two actions should they take? (Choose two.)
⚠ Common exam trap
The trap here is assuming that preemptible VMs are suitable for all cost-saving scenarios, but they can be terminated and are not appropriate for high-availability production workloads.
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
✓
Purchase committed use discounts for the GKE nodes' underlying Compute Engine instances.
Enabling cluster autoscaler ensures the GKE cluster scales dynamically with demand, reducing costs during idle periods while maintaining performance. Purchasing committed use discounts for the underlying Compute Engine instances provides cost savings for the baseline load. Together, these actions optimize costs without compromising performance or availability.
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 preemptible VMs for the GKE nodes to reduce compute costs.
Why it's wrong here
Preemptible VMs are significantly cheaper but can be terminated at any time, which may cause disruptions for a high-traffic web application that requires high availability. They are not suitable for production workloads that cannot tolerate interruptions. Therefore, this action would sacrifice availability.
- ✓
Purchase committed use discounts for the GKE nodes' underlying Compute Engine instances.
Why this is correct
Committed use discounts (CUDs) provide significant savings for steady-state usage by committing to a certain amount of resources for 1 or 3 years. For a high-traffic application with predictable baseline load, purchasing CUDs for the GKE nodes can reduce compute costs without affecting performance or availability.
- ✗
Configure Cloud SQL to use a shared-core machine type to reduce database costs.
Why it's wrong here
Shared-core machine types are designed for low-traffic or testing environments and do not provide the performance needed for a high-traffic web application. Using them would likely degrade performance and is not appropriate for production workloads with high demand.
- ✗
Schedule regular backups of the Cloud SQL instance to reduce storage costs.
Why it's wrong here
Backups are essential for data protection but do not directly reduce the cost of the Cloud SQL instance. In fact, backups incur additional storage costs. This action does not optimize the cost of the database or cluster in terms of compute and memory resources.
- ✓
Enable GKE cluster autoscaler to automatically adjust the number of nodes based on workload demand.
Why this is correct
Cluster autoscaler adds or removes nodes based on pod resource requests, ensuring the cluster scales with demand. This reduces cost during low usage while maintaining performance during peaks. It is a recommended practice for optimizing GKE costs without sacrificing availability.
Go deeper
Related to this question
Learn chapter
Billing, Budgets, and Cost Management
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
Key term
Compute Engine
Compute Engine is Google Cloud's Infrastructure-as-a-Service (IaaS) offering that lets you create and run virtual machines on Google's infrastructure.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.