Cloud Digital Leader Scaling with Google Cloud operations Practice Question
This GCDL practice question tests your understanding of scaling with google cloud operations. 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.
A company runs a web application on Google Kubernetes Engine (GKE) that experiences sudden traffic spikes. The operations team notices that the application's response time increases significantly during these spikes despite having Horizontal Pod Autoscaler (HPA) configured. They want to ensure consistent performance. What should they do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the HPA to use custom metrics based on request latency.
Option B is correct because configuring the HPA to use custom metrics based on request latency allows the autoscaler to react directly to the application's performance degradation. Unlike CPU-based metrics, which may not reflect actual user-facing latency during traffic spikes, custom metrics like request latency provide a more accurate signal for scaling decisions, ensuring consistent response times.
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 CPU request limit for all pods.
Why it's wrong here
Increasing CPU request limits does not help the HPA scale based on latency; it only reserves more CPU per pod.
✓
Configure the HPA to use custom metrics based on request latency.
Why this is correct
Custom metrics like request latency allow the HPA to scale pods based on actual application performance, improving responsiveness during spikes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create multiple node pools with different machine types.
Why it's wrong here
Node pools address heterogeneous workloads but do not improve pod autoscaling based on latency.
✗
Manually scale the deployment during expected spikes.
Why it's wrong here
Manual scaling is not automated and may not react quickly enough to sudden traffic spikes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that CPU-based HPA is sufficient for all scaling scenarios, but the trap here is that CPU metrics do not capture application-level performance degradation caused by request latency or queue buildup during traffic spikes.
Detailed technical explanation
How to think about this question
Custom metrics in GKE are exposed via the Kubernetes Metrics Server or Prometheus adapter, allowing HPA to query metrics like request latency from application endpoints (e.g., via Istio or custom instrumentation). Under the hood, the HPA controller calculates the desired replica count using the formula: desiredReplicas = currentReplicas * (currentMetricValue / targetMetricValue). This enables proactive scaling based on actual user experience rather than indirect CPU utilization, which can lag behind traffic spikes due to request queuing or inefficient code paths.
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 GCDL question in full detail.
Scaling with Google Cloud operations — This question tests Scaling with Google Cloud operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the HPA to use custom metrics based on request latency. — Option B is correct because configuring the HPA to use custom metrics based on request latency allows the autoscaler to react directly to the application's performance degradation. Unlike CPU-based metrics, which may not reflect actual user-facing latency during traffic spikes, custom metrics like request latency provide a more accurate signal for scaling decisions, ensuring consistent response times.
What should I do if I get this GCDL 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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Question Discussion
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