mediumMultiple ChoiceObjective-mapped
Google ACE Practice Question: A team's GKE Deployment serves variable traffic —…
A team's GKE Deployment serves variable traffic — 2 Pods at night, 20 Pods at peak hours. Rather than manually changing replica counts, they want automatic scaling based on CPU utilization (target: 60%). What should they deploy?
⚠ Common exam trap
Google Cloud often tests the distinction between scaling Pod replicas (HPA) versus scaling Pod resources (VPA) versus scaling cluster nodes (Cluster Autoscaler), and the trap here is confusing VPA's resource adjustment with HPA's replica adjustment, especially when the question mentions 'CPU utilization target'.
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
✓
Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization
The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically adjusts the number of Pod replicas in a Deployment based on observed CPU utilization, scaling from 2 to 20 Pods as needed to maintain the target of 60% CPU. HPA works by querying the metrics server for CPU usage and calculating the desired replica count using the formula: desiredReplicas = currentReplicas × (currentMetricValue / targetMetricValue). This directly addresses the requirement for variable traffic 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.
- ✗
Vertical Pod Autoscaler (VPA) with CPU target 60%
Why it's wrong here
The Vertical Pod Autoscaler (VPA) modifies the CPU and memory resource requests and limits on individual Pods, and it may restart Pods to apply those changes, but it never changes the number of replicas running. A CPU target of 60% would serve as a recommendation for resizing the Pod's resource allocations, not as a threshold for scaling the replica count. To add or remove Pods based on CPU load, you must use an HPA, not a VPA.
- ✓
Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization
Why this is correct
The Horizontal Pod Autoscaler (HPA) automatically adjusts the number of Pod replicas in a Deployment based on observed CPU utilization relative to the CPU requests set on containers. When average CPU utilization exceeds the 60% target, HPA increases the replica count; when it drops below the target for a sustained period, it decreases replicas. This directly matches the requirement to scale Pod count based on load, and it is the standard Kubernetes mechanism for horizontal scaling of workloads.
- ✗
Cluster Autoscaler with a CPU threshold of 60%
Why it's wrong here
Cluster Autoscaler resizes the node pool by adding or removing nodes, not by changing the replica count of a Deployment. It responds to unschedulable Pods or underutilized nodes based on cluster-wide resource requests, and it does not accept a CPU utilization threshold of 60% as a scaling trigger. A CPU-based threshold that adjusts the number of Pod replicas falls under HPA's responsibility; Cluster Autoscaler operates at the infrastructure layer and would leave the replica count untouched.
- ✗
Set the Deployment replica count to 20 and rely on resource quotas to limit actual Pod scheduling
Why it's wrong here
Setting a static replica count of 20 does not respond to dynamic changes in CPU utilization or traffic; the Deployment will always run exactly 20 Pods regardless of actual load, wasting resources during idle periods. Resource quotas only enforce a hard limit on the total CPU and memory consumed by the namespace, and when quotas are reached, Pod scheduling simply fails rather than triggering a scale-in. This approach lacks any adaptive logic and is not a replacement for an autoscaler.
Go deeper
Related to this question
Learn chapter
Deployment Manager and Terraform on GCP
Key term
Autoscaler
An Autoscaler is a cloud service that automatically increases or decreases the number of virtual machines (instances) or resources based on real-time demand, so your application always has enough capacity without wasting money on idle servers.
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
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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.