The answer is that the HPA’s target CPU utilization is set too high, causing the autoscaler to react slowly. This is because the HorizontalPodAutoscaler only triggers a scale-up when the average CPU utilization across pods exceeds the configured target; if that target is set too high, the system waits until pods are already heavily saturated before adding capacity, which introduces a delay that manifests as high latency during peak hours. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of HPA threshold tuning and the relationship between resource saturation and autoscaling responsiveness—a common trap is assuming any CPU-based HPA will react instantly, when in fact a high target creates a lag that worsens latency for CPU-bound services. A useful memory tip is “high target, slow response”—if your CPU target is too high, your pods will suffer before the HPA kicks in.
PCDOE Optimizing service performance Practice Question
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.
Your team has deployed a microservices application on Google Kubernetes Engine (GKE). You notice that one service has high latency during peak hours. The service is CPU-bound and uses a HorizontalPodAutoscaler (HPA) based on CPU utilization. What is the most likely cause of the latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The HPA's target CPU utilization is set too high, causing the autoscaler to react slowly.
Option B is correct because when the HPA's target CPU utilization is set too high, the autoscaler waits until the average CPU utilization exceeds that threshold before scaling up. During peak hours, the service becomes CPU-bound and latency increases as pods are overwhelmed, but the HPA reacts slowly because it only triggers when the high threshold is breached, causing a delay in adding new pods to handle the load.
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.
✗
The GKE cluster uses preemptible nodes that are frequently reclaimed.
Why it's wrong here
Preemptible nodes cause pod evictions, not gradual latency increase.
✓
The HPA's target CPU utilization is set too high, causing the autoscaler to react slowly.
Why this is correct
A high target CPU threshold delays scaling, leading to latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The service uses a global external HTTP(S) load balancer with session affinity.
Why it's wrong here
Session affinity does not cause latency; it routes requests to the same backend.
✗
The application does not implement request autoscaling at the application layer.
Why it's wrong here
Request autoscaling is not a built-in GKE concept.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that HPA scaling is instantaneous or that CPU-bound latency is caused by external factors like load balancers or node preemption, when the real issue is the HPA threshold configuration and its delayed reaction to sustained high utilization.
Detailed technical explanation
How to think about this question
The HorizontalPodAutoscaler in GKE uses the metrics-server to collect CPU utilization metrics every 15 seconds, but the default behavior includes a cooldown period (--horizontal-pod-autoscaler-downscale-stabilization) and a scaling decision is made only when the current utilization exceeds the target for a sustained period. If the target CPU utilization is set to 90%, the HPA will not scale up until pods are consistently above 90%, meaning latency spikes occur before the threshold is crossed, especially under bursty traffic patterns. In production, setting the target too high (e.g., 80-90%) is a common misconfiguration that leads to performance degradation before the autoscaler reacts.
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.
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: The HPA's target CPU utilization is set too high, causing the autoscaler to react slowly. — Option B is correct because when the HPA's target CPU utilization is set too high, the autoscaler waits until the average CPU utilization exceeds that threshold before scaling up. During peak hours, the service becomes CPU-bound and latency increases as pods are overwhelmed, but the HPA reacts slowly because it only triggers when the high threshold is breached, causing a delay in adding new pods to handle the load.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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