This PCDOE practice question tests your understanding of optimizing service performance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A DevOps team wants to optimize resource utilization for their GKE deployment. Which built-in Kubernetes resource can automatically adjust CPU and memory requests based on historical usage?
HorizontalPodAutoscaler
Why wrong: HPA scales replicas, not resource requests.
ResourceQuota
Why wrong: ResourceQuota enforces limits on namespace resources.
PodDisruptionBudget
Why wrong: PodDisruptionBudget controls voluntary disruptions.
VerticalPodAutoscaler
Correct. VPA adjusts resource requests based on usage.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
VerticalPodAutoscaler
The VerticalPodAutoscaler (VPA) is the correct choice because it automatically adjusts CPU and memory resource requests (and limits) for pods based on historical usage data, optimizing resource utilization without manual intervention. Unlike the HorizontalPodAutoscaler, which scales the number of pods, the VPA modifies the resource specifications of existing pods to match observed demand.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
HorizontalPodAutoscaler
Why it's wrong here
HPA scales replicas, not resource requests.
ResourceQuota
Why it's wrong here
ResourceQuota enforces limits on namespace resources.
PodDisruptionBudget
Why it's wrong here
PodDisruptionBudget controls voluntary disruptions.
VerticalPodAutoscaler
Why this is correct
Correct. VPA adjusts resource requests based on usage.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the distinction between scaling the number of replicas (HPA) versus scaling the resources per pod (VPA), and candidates mistakenly choose HPA because they associate 'automatic adjustment' with scaling out, not adjusting requests.
Detailed technical explanation
The VPA uses a recommender component that analyzes historical metrics from the Metrics Server (e.g., CPU and memory utilization percentiles over a configurable lookback window, default 8 days) and suggests updated resource requests. It then updates the pod's resource spec via a mutating admission webhook, which can recreate the pod if necessary (in 'Auto' mode) or only provide recommendations (in 'Off' mode). A real-world scenario is a stateful workload with variable usage patterns, where VPA prevents over-provisioning and reduces cluster costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
The correct answer is: VerticalPodAutoscaler — The VerticalPodAutoscaler (VPA) is the correct choice because it automatically adjusts CPU and memory resource requests (and limits) for pods based on historical usage data, optimizing resource utilization without manual intervention. Unlike the HorizontalPodAutoscaler, which scales the number of pods, the VPA modifies the resource specifications of existing pods to match observed demand.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PCDOE 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 PCDOE exam.
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