The answer is to configure Horizontal Pod Autoscaler (HPA) based on CPU utilization or custom metrics. This is correct because the cluster has headroom—meaning node-level CPU and memory are sufficient—so the bottleneck is at the pod level, where a fixed number of replicas cannot handle the increased request volume. HPA dynamically adjusts the replica count to distribute the traffic spike across more pods, directly resolving slowdowns and timeouts. On the Google Professional Cloud Architect exam, this scenario tests your ability to distinguish between cluster-level scaling (Cluster Autoscaler) and pod-level scaling (HPA); a common trap is assuming headroom means no scaling is needed, when in fact the application itself is starved for replicas. Remember: Cluster Autoscaler adds nodes, HPA adds pods—when your GKE application slows during a traffic spike but the cluster has headroom, think “pods, not nodes.” A handy mnemonic: “Headroom in the cluster, HPA is the master.”
Google PCA Ensure solution and operations reliability Practice Question
This PCA practice question tests your understanding of ensure solution and operations reliability. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company runs a web application on Google Kubernetes Engine (GKE) with Cluster Autoscaler enabled. During a traffic spike, the application becomes slow and some requests timeout. The cluster has sufficient CPU and memory headroom. What is the most likely cause and solution?
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
✓
Configure Horizontal Pod Autoscaler (HPA) based on CPU utilization or custom metrics.
The correct answer is D because the cluster has sufficient CPU and memory headroom, indicating that the issue is not about cluster capacity but about pod-level scaling. The Horizontal Pod Autoscaler (HPA) automatically scales the number of pod replicas based on observed CPU utilization or custom metrics, which directly addresses the application slowdown and timeouts during traffic spikes by distributing the load across more pods.
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 node pool's machine type to a larger size.
Why it's wrong here
Larger nodes don't help if the application pods are not configured to scale horizontally.
✗
Enable Cluster Autoscaler to add more nodes.
Why it's wrong here
Cluster Autoscaler adds nodes but doesn't scale pods; the issue is pod-level scaling.
✗
Deploy the application in a regional cluster for higher availability.
Why it's wrong here
Regional clusters improve availability but do not address scalability during spikes.
✓
Configure Horizontal Pod Autoscaler (HPA) based on CPU utilization or custom metrics.
Why this is correct
HPA automatically scales pods based on load, resolving the timeout issue.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between node-level scaling (Cluster Autoscaler) and pod-level scaling (HPA), trapping candidates who assume that adding more nodes is the solution when the cluster already has headroom, whereas the real issue is insufficient pod replicas to handle the load.
Detailed technical explanation
How to think about this question
The Horizontal Pod Autoscaler (HPA) works by querying the Kubernetes Metrics Server for resource utilization metrics (e.g., CPU or memory) or custom metrics from services like Prometheus, then adjusting the `replicas` field in the Deployment or StatefulSet. A common subtlety is that HPA requires the Metrics Server to be deployed and running in the cluster; without it, HPA cannot collect metrics and will not scale. In real-world scenarios, if the application is CPU-bound but the HPA target is set too high, or if custom metrics are not properly exposed, the autoscaler may fail to trigger, leading to performance issues even with ample node resources.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this PCA question in full detail.
Ensure solution and operations reliability — This question tests Ensure solution and operations reliability — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure Horizontal Pod Autoscaler (HPA) based on CPU utilization or custom metrics. — The correct answer is D because the cluster has sufficient CPU and memory headroom, indicating that the issue is not about cluster capacity but about pod-level scaling. The Horizontal Pod Autoscaler (HPA) automatically scales the number of pod replicas based on observed CPU utilization or custom metrics, which directly addresses the application slowdown and timeouts during traffic spikes by distributing the load across more pods.
What should I do if I get this PCA 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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