- A
Enable autoscaling based on CPU utilization
Autoscaling adds instances during load spikes, maintaining low latency without sacrificing accuracy.
- B
Use a larger machine type
Why wrong: Larger machines may handle more requests but are not elastic and may still experience latency during spikes; also increases cost.
- C
Reduce model size by pruning
Why wrong: Pruning may reduce latency but often sacrifices model accuracy, which is not acceptable.
- D
Implement client-side caching
Why wrong: Caching only helps for repeated identical requests; unique requests during peaks still experience latency.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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.
A company deploys a model on Vertex AI Endpoints for real-time inference. They notice latency spikes during peak hours. Which action is most effective to reduce latency without sacrificing accuracy?
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
Enable autoscaling based on CPU utilization
Option B is correct because enabling autoscaling based on CPU utilization dynamically adjusts the number of instances to handle traffic spikes, reducing latency. Option A increases cost without addressing scaling elasticity. Option C may help but not during peaks if requests are unique. Option D can reduce accuracy.
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.
- ✓
Enable autoscaling based on CPU utilization
Why this is correct
Autoscaling adds instances during load spikes, maintaining low latency without sacrificing accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger machine type
Why it's wrong here
Larger machines may handle more requests but are not elastic and may still experience latency during spikes; also increases cost.
- ✗
Reduce model size by pruning
Why it's wrong here
Pruning may reduce latency but often sacrifices model accuracy, which is not acceptable.
- ✗
Implement client-side caching
Why it's wrong here
Caching only helps for repeated identical requests; unique requests during peaks still experience latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
Serving and scaling models — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable autoscaling based on CPU utilization — Option B is correct because enabling autoscaling based on CPU utilization dynamically adjusts the number of instances to handle traffic spikes, reducing latency. Option A increases cost without addressing scaling elasticity. Option C may help but not during peaks if requests are unique. Option D can reduce accuracy.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 24, 2026
This PMLE 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 PMLE exam.
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