Google PCA Manage and provision cloud infrastructure Practice Question
A company is deploying a new application on Google Kubernetes Engine (GKE). They need to ensure that the application can automatically scale based on custom metrics, such as the number of pending requests in a queue. They also want to minimize operational overhead. Which TWO actions should they take? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse Horizontal Pod Autoscaler with Vertical Pod Autoscaler or Cluster Autoscaler, and forgetting that custom metrics must be exported to Cloud Monitoring first.
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
✓
Export the custom metric to Cloud Monitoring using the Cloud Monitoring API or a sidecar.
To scale based on custom metrics in GKE, the metrics must be exported to Cloud Monitoring, and then the Horizontal Pod Autoscaler can be configured to use those metrics. This approach leverages managed GKE features, minimizing operational overhead. Cluster Autoscaler and VPA address different scaling dimensions and are not required for custom metric scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Kubernetes Cluster Autoscaler to add nodes when pods are pending.
Why it's wrong here
Cluster Autoscaler adjusts the number of nodes in the node pool based on pod resource requests. While it helps with scaling infrastructure, it does not scale pods based on custom metrics. It is complementary but not directly required for custom metric scaling. The scenario asks for scaling based on custom metrics, not node scaling.
- ✗
Configure a Vertical Pod Autoscaler (VPA) to adjust resource requests.
Why it's wrong here
VPA adjusts CPU and memory requests for pods, not the number of replicas. It does not scale based on custom metrics like queue length. While it can optimize resource usage, it does not meet the requirement for scaling out based on custom metrics. VPA is also not ideal for applications that need horizontal scaling.
- ✗
Deploy the application as a DaemonSet to ensure one pod per node.
Why it's wrong here
A DaemonSet runs a copy of a pod on each node, typically for system daemons. It does not provide horizontal scaling based on metrics and is not suitable for a scalable application. This would not help with custom metric scaling and increases operational overhead by managing pods per node.
- ✓
Export the custom metric to Cloud Monitoring using the Cloud Monitoring API or a sidecar.
Why this is correct
For HPA to use custom metrics, they must be available in Cloud Monitoring. This can be done via the Cloud Monitoring API or by using a sidecar like the Stackdriver adapter. Exporting the metric is a prerequisite for HPA to scale based on it. This action, combined with enabling HPA, fulfills the requirement.
- ✓
Enable Horizontal Pod Autoscaler (HPA) with custom metrics from Cloud Monitoring.
Why this is correct
HPA can scale pods based on custom metrics exported to Cloud Monitoring. This allows scaling based on queue length or other application-specific metrics. It is a managed feature of GKE, so operational overhead is low. This action directly addresses the requirement to scale based on custom metrics.
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Key term
CAN
A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
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 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.