Google PCA Practice Question: Managing and Provisioning a Solution Infrastructure
A GKE cluster has a Horizontal Pod Autoscaler (HPA) configured for CPU utilization. The pods are not scaling up even though CPU usage is high. What could be the reason?
⚠ Common exam trap
PCA often tests the HPA utilization formula — candidates assume HPA scales on raw CPU usage, but it actually scales on usage relative to the pod's CPU request, so missing requests silently break scaling.
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
✓
The pods do not have resource requests defined
HPA for CPU utilization calculates utilization as a percentage of the pod's CPU request, not of the node or a raw usage value. If pods have no resource requests defined, the HPA cannot compute a utilization percentage and will not scale, even when CPU usage is high. Defining CPU requests on the pods resolves this.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The cluster autoscaler is disabled
Why it's wrong here
Disabling the cluster autoscaler stops nodes being added, but the HPA would still raise the deployment's replica count; the failure described is that replicas never increase. The cluster autoscaler provisions nodes when pods sit Pending, so it would be the answer if pods were scaling but stuck unscheduled.
- ✗
The HPA is configured with the wrong metric name
Why it's wrong here
A wrong metric name would surface as an HPA error condition (FailedGetResourceMetric) rather than silent non-scaling, and CPU utilisation is a built-in resource metric requiring no name. Custom and external metrics need explicit names, so this fits an HPA reading a custom metric, not CPU.
- ✗
The node pool is out of capacity
Why it's wrong here
Insufficient node capacity leaves new pods Pending, yet the HPA still increments the replica count, so the symptom would be unschedulable pods rather than no scaling. Node capacity is the constraint when the HPA scales successfully but the cluster autoscaler cannot add nodes.
- ✓
The pods do not have resource requests defined
Why this is correct
Without CPU resource requests, the HPA cannot calculate utilisation as a percentage of the requested amount, so it treats the metric as unavailable and refuses to scale. Defining requests on the container spec satisfies the HPA's prerequisite for computing the target utilisation ratio.
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Related to this question
Learn chapter
Google Cloud Resource Hierarchy and Organization
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.
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.
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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
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