The answer is to change the autoscaling metric to average request count per replica with an appropriate target. This improves autoscaling responsiveness for traffic spikes because request count is a direct, leading indicator of load, whereas CPU utilization is a lagging metric that only rises after requests have already begun processing, causing delays in scaling. On the Google Professional Machine Learning Engineer exam, this tests your understanding of Vertex AI endpoint configuration and the trade-offs between reactive and predictive scaling signals; a common trap is assuming higher CPU target utilization speeds things up, but that actually makes scaling slower by raising the threshold. Remember the memory tip: “Count the requests, not the CPU stress” — request-based scaling anticipates spikes, while CPU-based scaling reacts to them.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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 model serving team notices that during a flash sale, a real-time recommendation model experiences sudden spikes in traffic, causing some requests to time out. The endpoint is configured with `min_replica_count=3`, `max_replica_count=10`, and autoscaling metric set to `target_utilization=0.6` on CPU. Despite this, autoscaling is too slow. What change will most improve the autoscaling responsiveness?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the autoscaling metric to 'average request count per replica' with an appropriate target.
Option A is correct because using request count per replica (transactions per second) as a direct measure of load triggers autoscaling faster. Option B is wrong because increasing target utilization makes it slower. Option C is wrong because GPU metrics are only relevant for GPU models. Option D is wrong because reducing min replicas may cause underprovisioning.
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.
✗
Add a custom metric based on GPU utilization, assuming the model uses GPU.
Why it's wrong here
The model is a recommendation model typically using CPU.
✗
Increase the target CPU utilization to 0.8 to reduce the number of replicas and save cost.
Why it's wrong here
Higher target utilization delays scaling up.
✗
Reduce `min_replica_count` to 1 to allow more aggressive scaling.
Why it's wrong here
Fewer base replicas may worsen initial spike handling.
✓
Change the autoscaling metric to 'average request count per replica' with an appropriate target.
Why this is correct
Request count directly reflects load and scales more quickly than CPU.
Related concept
Read the scenario before looking for a memorised answer.
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 — 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: Change the autoscaling metric to 'average request count per replica' with an appropriate target. — Option A is correct because using request count per replica (transactions per second) as a direct measure of load triggers autoscaling faster. Option B is wrong because increasing target utilization makes it slower. Option C is wrong because GPU metrics are only relevant for GPU models. Option D is wrong because reducing min replicas may cause underprovisioning.
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.
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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.
Question Discussion
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