- A
Increase the target CPU utilization percentage to 90% so that pods are less likely to be added.
Why wrong: Increasing target CPU utilization actually delays scaling because it raises the threshold for scaling up.
- B
Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency).
Custom metrics like request latency provide a more direct and responsive signal to scale based on performance.
- C
Set the maxReplicas of the HPA to a value lower than the expected peak traffic to force faster scaling.
Why wrong: Lowering max replicas caps capacity and could lead to insufficient resources during bursts, worsening performance.
- D
Reduce the --horizontal-pod-autoscaler-upscale-stabilization window in the HPA configuration to a lower value (e.g., 30 seconds).
A smaller stabilization window allows the HPA to react faster to spikes, reducing latency.
- E
Increase the --horizontal-pod-autoscaler-sync-period flag to 30 seconds and increase the --horizontal-pod-autoscaler-upscale-delay flag to 5 minutes.
Why wrong: Increasing cooldown delays makes autoscaling less responsive, worsening latency spikes.
Quick Answer
The answer is to reduce the `--horizontal-pod-autoscaler-upscale-stabilization` window and to use a custom metric based on request latency. Reducing the stabilization window from the default five minutes to thirty seconds directly addresses the delay in HPA scaling responsiveness on GKE, allowing the autoscaler to act on scale-up recommendations much faster during traffic bursts. Meanwhile, relying solely on CPU utilization often lags behind real-world load, as latency spikes typically precede CPU saturation; a custom metric like p99 latency provides a more immediate signal for scaling. On the Google Professional Cloud DevOps Engineer exam, this question tests your understanding of HPA tuning parameters and the limitations of resource-based metrics—a common trap is thinking only CPU or memory metrics are sufficient. Remember the memory tip: "Stabilization is the brake, custom metrics are the accelerator"—to improve HPA scaling responsiveness, you must release the brake and switch to a faster pedal.
PCDOE Optimizing service performance Practice Question
This PCDOE practice question tests your understanding of optimizing service performance. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Your team is running a high-traffic web application on Google Kubernetes Engine (GKE) and has configured Horizontal Pod Autoscaling (HPA) based on CPU utilization. Recently, the application experienced intermittent latency spikes during traffic bursts. You suspect that the HPA is not scaling quickly enough. Which TWO actions would most effectively improve the autoscaling responsiveness?
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
Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency).
Option B is correct because using a custom metric based on request latency (e.g., p99 latency) allows HPA to react to application performance degradation directly, rather than relying solely on CPU utilization which may lag behind traffic bursts. This provides a more immediate signal for scaling, as latency spikes often precede CPU saturation in web applications. Option D is correct because reducing the --horizontal-pod-autoscaler-upscale-stabilization window (default 5 minutes) to a lower value like 30 seconds decreases the time HPA waits before acting on scale-up recommendations, enabling faster response to sudden load increases.
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.
- ✗
Increase the target CPU utilization percentage to 90% so that pods are less likely to be added.
Why it's wrong here
Increasing target CPU utilization actually delays scaling because it raises the threshold for scaling up.
- ✓
Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency).
Why this is correct
Custom metrics like request latency provide a more direct and responsive signal to scale based on performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the maxReplicas of the HPA to a value lower than the expected peak traffic to force faster scaling.
Why it's wrong here
Lowering max replicas caps capacity and could lead to insufficient resources during bursts, worsening performance.
- ✓
Reduce the --horizontal-pod-autoscaler-upscale-stabilization window in the HPA configuration to a lower value (e.g., 30 seconds).
Why this is correct
A smaller stabilization window allows the HPA to react faster to spikes, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the --horizontal-pod-autoscaler-sync-period flag to 30 seconds and increase the --horizontal-pod-autoscaler-upscale-delay flag to 5 minutes.
Why it's wrong here
Increasing cooldown delays makes autoscaling less responsive, worsening latency spikes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing target CPU utilization or sync periods improves scaling speed, when in fact these changes reduce responsiveness; the trap is confusing 'stabilization' with 'delay' and assuming higher thresholds or longer intervals help with bursts.
Detailed technical explanation
How to think about this question
The HPA stabilization window (--horizontal-pod-autoscaler-upscale-stabilization) is a Kubernetes feature that prevents flapping by requiring the recommended replica count to remain stable for the window duration before scaling; reducing it trades stability for faster reaction. Custom metrics in HPA are collected via the custom.metrics.k8s.io API, which can be backed by Prometheus Adapter or Google Cloud Monitoring, allowing latency percentiles to trigger scaling before CPU utilization rises. In real-world scenarios, CPU-based HPA often fails to prevent latency spikes because modern applications may hit concurrency limits or queue depths before CPU usage increases significantly.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Optimizing service performance — study guide chapter
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FAQ
Questions learners often ask
What does this PCDOE question test?
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency). — Option B is correct because using a custom metric based on request latency (e.g., p99 latency) allows HPA to react to application performance degradation directly, rather than relying solely on CPU utilization which may lag behind traffic bursts. This provides a more immediate signal for scaling, as latency spikes often precede CPU saturation in web applications. Option D is correct because reducing the --horizontal-pod-autoscaler-upscale-stabilization window (default 5 minutes) to a lower value like 30 seconds decreases the time HPA waits before acting on scale-up recommendations, enabling faster response to sudden load increases.
What should I do if I get this PCDOE 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.
About these practice questions
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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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