- A
It eliminates the need for a load balancer.
Why wrong: Autoscaling does not remove the need for load balancing.
- B
It reduces costs by scaling down to zero replicas when no requests are received.
Scale-to-zero minimizes cost for low-traffic endpoints.
- C
It reduces model training time.
Why wrong: Autoscaling is for serving, not training.
- D
It automatically upgrades the model version.
Why wrong: Autoscaling does not handle model version upgrades.
Question 24 of 1,000
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.
Which of the following is a benefit of using Vertex AI Endpoints with autoscaling and scale-to-zero?
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
✓
It reduces costs by scaling down to zero replicas when no requests are received.
Vertex AI Endpoints with autoscaling and scale-to-zero allow the number of serving replicas to dynamically adjust based on incoming traffic. When no requests are received, the endpoint can scale down to zero replicas, meaning you are not charged for idle compute resources. This directly reduces operational costs compared to maintaining a minimum number of always-on instances.
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.
- ✗
It eliminates the need for a load balancer.
Why it's wrong here
Autoscaling does not remove the need for load balancing.
- ✓
It reduces costs by scaling down to zero replicas when no requests are received.
Why this is correct
Scale-to-zero minimizes cost for low-traffic endpoints.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
It reduces model training time.
Why it's wrong here
Autoscaling is for serving, not training.
- ✗
It automatically upgrades the model version.
Why it's wrong here
Autoscaling does not handle model version upgrades.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that autoscaling eliminates the need for a load balancer, but in Vertex AI Endpoints, the load balancer is a separate component that remains essential for request distribution even when scaling to zero.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Endpoints use the Google Cloud Load Balancer and the Kubernetes-based AI Platform Prediction service to manage replica scaling. The autoscaler monitors metrics such as CPU utilization and request latency, and when the target metric drops below a threshold for a sustained period, it reduces the number of replicas, eventually to zero. A real-world scenario is a batch prediction job that runs once a day: with scale-to-zero, the endpoint incurs no cost for the remaining 23 hours, whereas a fixed-minimum deployment would incur continuous charges.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: It reduces costs by scaling down to zero replicas when no requests are received. — Vertex AI Endpoints with autoscaling and scale-to-zero allow the number of serving replicas to dynamically adjust based on incoming traffic. When no requests are received, the endpoint can scale down to zero replicas, meaning you are not charged for idle compute resources. This directly reduces operational costs compared to maintaining a minimum number of always-on instances.
What should I do if I get this PMLE 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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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