easyMultiple ChoiceObjective-mapped
Google ACE Practice Question: Deploy a containerized web application on Google…
A company wants to deploy a containerized web application on Google Kubernetes Engine (GKE) with minimal operational overhead. They require automatic scaling based on CPU utilization. Which resource should they configure?
⚠ Common exam trap
Google Cloud often tests the distinction between horizontal scaling (adding/removing pods) and vertical scaling (adjusting pod resources) or infrastructure scaling (adding/removing nodes), leading candidates to confuse the HorizontalPodAutoscaler with the Cluster autoscaler or VerticalPodAutoscaler.
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
✓
HorizontalPodAutoscaler
The HorizontalPodAutoscaler (HPA) is the correct resource because it automatically scales the number of pod replicas in a GKE deployment based on observed CPU utilization (or other custom metrics). This directly meets the requirement for automatic scaling with minimal operational overhead, as HPA is a native Kubernetes controller that adjusts replica counts without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cluster autoscaler
Why it's wrong here
Cluster autoscaler operates at the infrastructure layer, adding or removing nodes from a GKE node pool when pods cannot be scheduled due to insufficient cluster capacity. It does not alter the desired replica count of a Deployment; if the web application is resource-constrained, cluster autoscaler only adds a machine, leaving the original pod count unchanged. This distinguishes it from HPA, which changes the number of pods themselves.
- ✗
VerticalPodAutoscaler
Why it's wrong here
VerticalPodAutoscaler adjusts the CPU and memory requests/limits of an existing pod's containers to match observed historical usage, often requiring a restart to take effect. It never changes the number of replicas running the application; instead it makes the few existing pods larger or smaller, which is the opposite of horizontal scaling. For a containerized web app that needs more replicas to handle load, VPA is an insufficient mechanism because it only tunes resource sizes.
- ✓
HorizontalPodAutoscaler
Why this is correct
HorizontalPodAutoscaler continuously monitors a selected metric, typically CPU utilization, and reconciles the 'replicas' field of a Deployment or ReplicaSet to the number needed to meet the target. When average CPU exceeds the configured threshold, HPA increases replica count; when demand drops, it decreases pods to help control cost. This is precisely the behavior required to scale a containerized web application horizontally based on workload pressure.
- ✗
Node auto-provisioning
Why it's wrong here
Node auto-provisioning, part of GKE's cluster autoscaler feature, automatically creates new node pools with machine types suited to pending pods and deletes idle ones, all at the cluster infrastructure level. It does not, on its own, change the number of application replicas; a pod must first be unschedulable before nodes are created. Thus, while it improves cluster capacity, it is not a mechanism for scaling the web application's pod count in response to CPU utilization.
Go deeper
Related to this question
Learn chapter
Google Cloud Platform Overview
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
Key term
Pod
A pod is the smallest deployable unit in Kubernetes, containing one or more containers that share storage, network, and a specification for how to run.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE 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 ACE exam.