- A
Vertical Pod Autoscaler (VPA) with CPU target 60%
Why wrong: VPA adjusts CPU/memory requests on individual Pods — it doesn't change the replica count. HPA handles replica scaling.
- B
Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization
HPA monitors CPU utilization and scales replicas up when average CPU exceeds 60% and down when it drops below — exactly the described behavior.
- C
Cluster Autoscaler with a CPU threshold of 60%
Why wrong: Cluster Autoscaler adds/removes nodes (not Pods) when Pods are Pending or nodes are underutilized — it operates at the node level, not the Deployment replica level.
- D
Set the Deployment replica count to 20 and rely on resource quotas to limit actual Pod scheduling
Why wrong: Resource quotas limit resource consumption but don't dynamically scale replica counts — this wastes resources during off-peak hours.
Quick Answer
The Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization is the correct choice because it automatically adjusts the number of Pod replicas in a GKE Deployment based on observed CPU metrics, scaling from 2 to 20 Pods as traffic varies without manual intervention. HPA works by querying the Kubernetes metrics server for current CPU usage and applying the formula desiredReplicas = currentReplicas × (currentMetricValue / targetMetricValue) to maintain the 60% target. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of autoscaling mechanics versus manual scaling or vertical scaling—a common trap is confusing HPA with Cluster Autoscaler, which adjusts node count, not Pod count. Remember that HPA handles Pod-level scaling based on resource utilization, while Cluster Autoscaler handles node-level scaling. A useful memory tip: “HPA keeps the Pod count in proportion to the CPU load, like a thermostat keeping a room at 60 degrees.”
Google ACE Deploying and implementing a cloud solution Practice Question
This ACE practice question tests your understanding of deploying and implementing a cloud solution. 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 team's GKE Deployment serves variable traffic — 2 Pods at night, 20 Pods at peak hours. Rather than manually changing replica counts, they want automatic scaling based on CPU utilization (target: 60%). What should they deploy?
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
Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization
The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically adjusts the number of Pod replicas in a Deployment based on observed CPU utilization, scaling from 2 to 20 Pods as needed to maintain the target of 60% CPU. HPA works by querying the metrics server for CPU usage and calculating the desired replica count using the formula: desiredReplicas = currentReplicas × (currentMetricValue / targetMetricValue). This directly addresses the requirement for variable traffic without manual intervention.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertical Pod Autoscaler (VPA) with CPU target 60%
Why it's wrong here
VPA adjusts CPU/memory requests on individual Pods — it doesn't change the replica count. HPA handles replica scaling.
- ✓
Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization
Why this is correct
HPA monitors CPU utilization and scales replicas up when average CPU exceeds 60% and down when it drops below — exactly the described behavior.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cluster Autoscaler with a CPU threshold of 60%
Why it's wrong here
Cluster Autoscaler adds/removes nodes (not Pods) when Pods are Pending or nodes are underutilized — it operates at the node level, not the Deployment replica level.
- ✗
Set the Deployment replica count to 20 and rely on resource quotas to limit actual Pod scheduling
Why it's wrong here
Resource quotas limit resource consumption but don't dynamically scale replica counts — this wastes resources during off-peak hours.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between scaling Pod replicas (HPA) versus scaling Pod resources (VPA) versus scaling cluster nodes (Cluster Autoscaler), and the trap here is confusing VPA's resource adjustment with HPA's replica adjustment, especially when the question mentions 'CPU utilization target'.
Detailed technical explanation
How to think about this question
Under the hood, HPA uses the `autoscaling/v2` API to support multiple metrics (CPU, memory, custom metrics) and can scale based on average utilization across all Pods. A subtle behavior is that HPA has a cooldown period (default 5 minutes for scale-up, 3 minutes for scale-down) to prevent thrashing, and it uses a stabilization window to smooth out fluctuations. In a real-world scenario, if the target CPU is 60% but Pods have bursty traffic, HPA may overshoot or undershoot due to the default metrics collection interval of 15 seconds, requiring tuning of `--horizontal-pod-autoscaler-sync-period`.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this ACE question test?
Deploying and implementing a cloud solution — This question tests Deploying and implementing a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Horizontal Pod Autoscaler (HPA) targeting 60% CPU utilization — The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically adjusts the number of Pod replicas in a Deployment based on observed CPU utilization, scaling from 2 to 20 Pods as needed to maintain the target of 60% CPU. HPA works by querying the metrics server for CPU usage and calculating the desired replica count using the formula: desiredReplicas = currentReplicas × (currentMetricValue / targetMetricValue). This directly addresses the requirement for variable traffic without manual intervention.
What should I do if I get this ACE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This ACE 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 ACE exam.
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