- A
Keep manual scaling but increase replicas to 10.
Why wrong: Manual scaling wastes resources during low traffic.
- B
Set min_replica_count=2 and max_replica_count=10 with no scaling metric.
Why wrong: Without a scaling metric, replicas stay at min, failing to handle spikes.
- C
Enable basic scaling with target_cpu_utilization=0.6 and set min_replica_count=2, max_replica_count=10.
Basic scaling adjusts replicas based on CPU load.
- D
Use custom metric scaling with a Cloud Monitoring metric for prediction latency.
Why wrong: Custom metric scaling is possible but not necessary; basic scaling works.
Question 698 of 1,000
mediumMultiple ChoiceObjective-mapped
Configuring Basic Autoscaling with CPU Utilization on Vertex AI Prediction
This PMLE practice question tests your understanding of pmle exam topics. 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 deploys a model on Vertex AI Endpoint and expects high traffic spikes during promotional events. The current configuration uses manual scaling with 2 replicas. Which autoscaling configuration should they use to handle spikes while minimizing cost during normal traffic?
Quick Answer
The correct choice is to enable basic autoscaling with a target CPU utilization of 0.6, setting a minimum of 2 replicas and a maximum of 10. This configuration works because basic autoscaling on Vertex AI Prediction dynamically adjusts the number of replicas based on the specified target metric—here, CPU utilization—so the endpoint scales up during traffic spikes and scales down when load decreases, directly minimizing cost during normal traffic while handling promotional events. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the trade-off between cost and performance for online predictions; a common trap is selecting manual scaling or no scaling, which cannot adapt to variable load, or overcomplicating with custom metrics when basic CPU-based scaling is sufficient for CPU-bound models. Remember the memory tip: “60% CPU is the sweet spot—scale up to ten, down to two, and let the load decide.”
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 basic scaling with target_cpu_utilization=0.6 and set min_replica_count=2, max_replica_count=10.
Option C is correct because basic scaling with a target CPU utilization (e.g., 0.6) automatically adjusts the number of replicas between min and max based on actual load. This allows the endpoint to scale up during traffic spikes (up to 10 replicas) to handle high demand, and scale down to the minimum (2 replicas) during normal traffic to minimize cost. Option A is wrong because manual scaling with fixed 10 replicas would incur unnecessary cost during low traffic and still may not handle extreme spikes if load exceeds 10 replicas. Option B is wrong because without a scaling metric, the endpoint cannot determine when to scale; it needs a metric like CPU utilization or request count. Option D is wrong because while custom metric scaling is possible, basic scaling with CPU utilization is simpler, sufficient, and recommended for CPU-bound models; the question asks for the configuration to handle spikes while minimizing cost, and basic scaling with CPU target achieves that.
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.
- ✗
Keep manual scaling but increase replicas to 10.
Why it's wrong here
Manual scaling wastes resources during low traffic.
- ✗
Set min_replica_count=2 and max_replica_count=10 with no scaling metric.
Why it's wrong here
Without a scaling metric, replicas stay at min, failing to handle spikes.
- ✓
Enable basic scaling with target_cpu_utilization=0.6 and set min_replica_count=2, max_replica_count=10.
Why this is correct
Basic scaling adjusts replicas based on CPU load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use custom metric scaling with a Cloud Monitoring metric for prediction latency.
Why it's wrong here
Custom metric scaling is possible but not necessary; basic scaling works.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Enable basic scaling with target_cpu_utilization=0.6 and set min_replica_count=2, max_replica_count=10. — Option C is correct because basic scaling with a target CPU utilization (e.g., 0.6) automatically adjusts the number of replicas between min and max based on actual load. This allows the endpoint to scale up during traffic spikes (up to 10 replicas) to handle high demand, and scale down to the minimum (2 replicas) during normal traffic to minimize cost. Option A is wrong because manual scaling with fixed 10 replicas would incur unnecessary cost during low traffic and still may not handle extreme spikes if load exceeds 10 replicas. Option B is wrong because without a scaling metric, the endpoint cannot determine when to scale; it needs a metric like CPU utilization or request count. Option D is wrong because while custom metric scaling is possible, basic scaling with CPU utilization is simpler, sufficient, and recommended for CPU-bound models; the question asks for the configuration to handle spikes while minimizing cost, and basic scaling with CPU target achieves that.
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
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