- A
Increase the HPA scale-up stabilization window to 10 minutes to prevent thrashing.
Why wrong: Longer stabilization window delays scaling, worsening latency during spikes.
- B
Enable session affinity on the backend service to keep users on the same pod.
Why wrong: Session affinity does not address scaling latency or resource bottlenecks.
- C
Set the HPA target CPU utilization to 70% and remove the scale-up stabilization window (set to 0 seconds).
Lower target triggers scaling earlier, and removing the stabilization window allows immediate scale-up when CPU exceeds the target, reducing latency spikes.
- D
Use a CronJob to proactively scale the backend service before expected peak hours.
Why wrong: CronJobs cannot react to sudden, unpredictable spikes; they only handle scheduled scaling.
Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
This GCDL practice question tests your understanding of google cloud products, services, and solutions. 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 financial services company runs a multi-tier application on Google Kubernetes Engine (GKE). The application consists of a frontend service, a backend service, and a database on Cloud SQL. Recently, they noticed that the backend service experiences high latency during peak trading hours, causing the frontend to time out. The backend service is CPU-intensive and currently runs with a single replica. The team wants to reduce latency and ensure high availability without over-provisioning resources. They have enabled Horizontal Pod Autoscaling (HPA) based on CPU utilization with a target of 80% and default stabilization windows. However, during sudden traffic spikes, the HPA takes over 5 minutes to scale up because of the scale-up stabilization window and the time to trigger. The company cannot tolerate latency spikes during scaling. Which course of action should they take to minimize latency during traffic spikes?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Set the HPA target CPU utilization to 70% and remove the scale-up stabilization window (set to 0 seconds).
Option C is correct because reducing the HPA target CPU utilization to 70% provides a larger buffer before the threshold is crossed, allowing earlier scaling initiation. Removing the scale-up stabilization window (setting it to 0 seconds) eliminates the default 5-minute delay, enabling the HPA to react immediately to CPU spikes. This directly addresses the latency issue during sudden traffic spikes by reducing the time to add replicas.
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 HPA scale-up stabilization window to 10 minutes to prevent thrashing.
Why it's wrong here
Longer stabilization window delays scaling, worsening latency during spikes.
- ✗
Enable session affinity on the backend service to keep users on the same pod.
Why it's wrong here
Session affinity does not address scaling latency or resource bottlenecks.
- ✓
Set the HPA target CPU utilization to 70% and remove the scale-up stabilization window (set to 0 seconds).
Why this is correct
Lower target triggers scaling earlier, and removing the stabilization window allows immediate scale-up when CPU exceeds the target, reducing latency spikes.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a CronJob to proactively scale the backend service before expected peak hours.
Why it's wrong here
CronJobs cannot react to sudden, unpredictable spikes; they only handle scheduled scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing stabilization windows or using proactive scheduling (CronJob) is the best way to handle sudden spikes, when in fact reducing or removing the scale-up delay and lowering the target utilization is the correct approach for minimizing latency under bursty traffic.
Detailed technical explanation
How to think about this question
The HPA scale-up stabilization window (default 5 minutes) is designed to prevent thrashing by requiring sustained high CPU before scaling, but this introduces a delay that is unacceptable for bursty workloads. Setting the stabilization window to 0 seconds makes the HPA react to the first metric observation that exceeds the target, which is ideal for latency-sensitive applications. The target CPU utilization of 70% (instead of 80%) creates a headroom of 30% before the threshold, allowing the HPA to initiate scaling earlier and reduce the risk of hitting the 100% ceiling where pods become overloaded.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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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Google Cloud products, services, and solutions — study guide chapter
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FAQ
Questions learners often ask
What does this GCDL question test?
Google Cloud products, services, and solutions — This question tests Google Cloud products, services, and solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the HPA target CPU utilization to 70% and remove the scale-up stabilization window (set to 0 seconds). — Option C is correct because reducing the HPA target CPU utilization to 70% provides a larger buffer before the threshold is crossed, allowing earlier scaling initiation. Removing the scale-up stabilization window (setting it to 0 seconds) eliminates the default 5-minute delay, enabling the HPA to react immediately to CPU spikes. This directly addresses the latency issue during sudden traffic spikes by reducing the time to add replicas.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 GCDL 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 GCDL exam.
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