Google PCA Manage and provision cloud infrastructure Practice Question
A company is deploying a microservices application on Google Kubernetes Engine (GKE). The architect needs to ensure that the cluster can automatically scale nodes based on pod resource requests and that pods are scheduled efficiently across nodes. The company also wants to minimize costs by scaling down when demand is low. Which two configurations should the architect implement? (Choose two.)
⚠ Common exam trap
The trap here is assuming that Horizontal Pod Autoscaler alone can scale nodes; it only scales pod replicas, not the underlying node pool.
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
✓
Enable Cluster Autoscaler on the node pool with a minimum and maximum node count.
Cluster Autoscaler scales the number of nodes in a node pool based on pending pod resource requests, and setting pod resource requests ensures that the scheduler and autoscaler have accurate information to make scaling decisions. Together, they enable automatic node scaling and efficient scheduling while allowing scale-down to reduce costs. The other options either address pod scaling, add unnecessary complexity, or improve availability without meeting the core requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Cluster Autoscaler on the node pool with a minimum and maximum node count.
Why this is correct
Cluster Autoscaler automatically adjusts the number of nodes in a node pool based on the resource requests of pending pods. It scales up when pods cannot be scheduled due to insufficient resources and scales down when nodes are underutilized. Setting a minimum and maximum node count ensures cost control and availability. This directly addresses the need to scale nodes based on pod demands and minimize costs during low demand.
- ✗
Configure Horizontal Pod Autoscaler (HPA) based on CPU utilization.
Why it's wrong here
Horizontal Pod Autoscaler scales the number of pod replicas based on metrics like CPU utilization, but it does not scale the number of nodes. While it helps handle increased load by adding pods, it does not directly address node scaling or efficient scheduling across nodes. HPA works in conjunction with Cluster Autoscaler, but alone it does not meet the requirement to scale nodes based on pod resource requests.
- ✓
Set pod resource requests and limits for CPU and memory.
Why this is correct
Setting accurate pod resource requests is essential for Cluster Autoscaler to make informed scaling decisions, as it uses these requests to determine if a node has enough capacity for pending pods. Limits help prevent resource contention. This configuration ensures that the scheduler can efficiently place pods and that autoscaling responds correctly to actual resource needs, supporting both efficient scheduling and cost optimization.
- ✗
Use a regional cluster with multiple zones.
Why it's wrong here
A regional cluster spreads nodes across multiple zones for high availability, but it does not automatically scale nodes based on pod resource requests. It also does not directly minimize costs during low demand; in fact, it may increase cost by maintaining nodes across zones. While useful for resilience, it does not fulfill the autoscaling and cost optimization requirements.
- ✗
Enable node auto-provisioning for the cluster.
Why it's wrong here
Node auto-provisioning automatically creates new node pools with optimal configurations based on pending pod requirements. While it can complement Cluster Autoscaler, it is not required to scale nodes based on pod resource requests; Cluster Autoscaler on existing node pools suffices. Auto-provisioning adds flexibility but may not be necessary and can introduce additional complexity or cost if not carefully managed.
Go deeper
Related to this question
Learn chapter
Google Kubernetes Engine (GKE)
Key term
Google Kubernetes Engine
Google Kubernetes Engine (GKE) is a managed Kubernetes service on Google Cloud that lets you deploy, scale, and manage containerized applications without having to operate the underlying cluster control plane.
Key term
Microservices
Microservices is an architectural style where a software application is built as a collection of small, independent services, each handling a specific business function and communicating over a network.
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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.