Google ACE Deploying and Implementing a Cloud Solution Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  CPU utilization metrics

  Why this is correct

  CPU utilization metrics drive the Horizontal Pod Autoscaler's scaling decisions. By default, HPA reads the average CPU utilization across all pods in a target Deployment, comparing it against the target percentage you set relative to each pod's CPU request. This is the most common and default metric type, requiring pods to have explicit `resources.requests.cpu` values. Custom and external metrics can be used, but CPU utilization is the built-in, standard input for autoscaling.

• ✗

  A Service

  Why it's wrong here

  A Service is not required for HPA to function; it exists to provide a stable network identity and load-balanced access to pods. HPA does not send traffic through a Service, nor does it need one to monitor metrics. Instead, the HPA controller queries the Kubernetes metrics API and directly updates the target workload's `scale` subresource. Services are relevant for exposing applications, not for determining replica counts.

• ✗

  A ConfigMap

  Why it's wrong here

  A ConfigMap is used to inject non-sensitive configuration data into containers, such as environment variables or configuration files. It plays no role in autoscaling, because HPA makes decisions based solely on observed metrics like CPU, memory, or custom application metrics. The HPA controller never reads ConfigMaps to decide how many replicas to maintain, so its absence or presence has no effect on scaling behavior. ConfigMaps are purely an application-level configuration mechanism.

• ✓

  A Deployment

  Why this is correct

  The Horizontal Pod Autoscaler acts on workloads that expose a scale subresource, with Deployment being the most common target. By updating the Deployment's replica count, HPA causes the Deployment controller to reconcile the underlying ReplicaSet and create or remove pods as needed. A Deployment provides the declarative template and versioning that make scaled changes safe and auditable. For most GKE autoscaling scenarios, you must have a Deployment (or equivalent like a StatefulSet) for HPA to manage.

• ✗

  A NodePort service

  Why it's wrong here

  A NodePort service is a variation of a Service that opens a specific static port on every node's IP address, used primarily for external access to your cluster. It is entirely unrelated to HPA functionality; autoscaling does not depend on how pods are exposed. Even if you remove all Services, HPA can still scale a Deployment based on metrics. Only networking aspects, such as reaching the application, would need a NodePort or another Service type.

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