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PROFESSIONAL-CLOUD-DEVOPS-ENGINEER · Question #139

You are performing a semi-annual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully con

The correct answer is A. Verify the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load. To prepare for predicted user growth and ensure zone failure resilience while avoiding unnecessary costs, verify the GKE node pool's maximum size, enable Horizontal Pod Autoscaling, and perform load testing to validate the scaling behavior and application performance.

Submitted by hans_de· Apr 18, 2026Applying site reliability engineering principles to a service

Question

You are performing a semi-annual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully containerized and runs on Google Cloud Platform (GCP), using a Google Kubernetes Engine (GKE) Standard regional cluster on three zones with cluster autoscaler enabled. You currently consume about 30% of your total deployed CPU capacity, and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth or as a result of zone failure, while avoiding unnecessary costs. How should you prepare to handle the predicted growth?

Options

  • AVerify the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load
  • BBecause you are deployed on GKE and are using a cluster autoscaler, your GKE cluster will
  • CBecause you are at only 30% utilization, you have significant headroom and you won't need to
  • DProactively add 60% more node capacity to account for six months of 10% growth rate, and then

How the community answered

(36 responses)
  • A
    61% (22)
  • B
    11% (4)
  • C
    6% (2)
  • D
    22% (8)

Why each option

To prepare for predicted user growth and ensure zone failure resilience while avoiding unnecessary costs, verify the GKE node pool's maximum size, enable Horizontal Pod Autoscaling, and perform load testing to validate the scaling behavior and application performance.

AVerify the maximum node pool size, enable a horizontal pod autoscaler, and then perform a loadCorrect

Verifying the maximum node pool size ensures the cluster autoscaler has sufficient capacity to expand, enabling Horizontal Pod Autoscaler allows pods to scale dynamically with application load (driven by growth), and a load test validates the entire system's resilience and performance under anticipated peak demand and failure scenarios.

BBecause you are deployed on GKE and are using a cluster autoscaler, your GKE cluster will

Relying solely on cluster autoscaler is insufficient; Horizontal Pod Autoscaler is needed to scale application pods, and the cluster autoscaler's maximum limits must be explicitly verified to accommodate growth and zone failure.

CBecause you are at only 30% utilization, you have significant headroom and you won't need to

30% current utilization does not account for the predicted 77% growth and the need to sustain 100% of the load with only two-thirds of the cluster's capacity available after a zone failure, indicating insufficient headroom.

DProactively add 60% more node capacity to account for six months of 10% growth rate, and then

Proactively adding fixed node capacity is inefficient and goes against avoiding unnecessary costs; dynamic scaling via HPA and Cluster Autoscaler with verified max limits is the preferred approach, and the 60% calculation is incorrect for 77% growth plus zone resilience.

Concept tested: GKE autoscaling, capacity planning, resilience

Source: https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler

Topics

#GKE Scaling#Capacity Planning#Resilience#Load Testing

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