- A
Deploy the model to a Compute Engine instance and use instance groups.
Why wrong: Compute Engine does not natively scale to zero; you would need to manage instances manually.
- B
Use a custom metric for autoscaling
Why wrong: Custom metrics can be used for scaling but do not enable scale-to-zero by themselves.
- C
Enable autoscaling with minReplicaCount=0
minReplicaCount=0 allows the endpoint to scale to zero when idle.
- D
Set maxReplicaCount to 0
Why wrong: maxReplicaCount=0 would prevent any scaling.
Question 956 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.
You need to deploy a model to a Vertex AI endpoint that can scale down to zero when there are no requests to minimize costs. Which feature should you enable?
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
✓
Enable autoscaling with minReplicaCount=0
Option C is correct because Vertex AI endpoints support autoscaling with a `minReplicaCount` of 0, which allows the endpoint to scale down to zero instances when there are no incoming requests, thereby minimizing costs. This feature is specifically designed for serverless model serving, where the endpoint automatically scales up from zero when traffic arrives and scales down to zero during idle periods.
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.
- ✗
Deploy the model to a Compute Engine instance and use instance groups.
Why it's wrong here
Compute Engine does not natively scale to zero; you would need to manage instances manually.
- ✗
Use a custom metric for autoscaling
Why it's wrong here
Custom metrics can be used for scaling but do not enable scale-to-zero by themselves.
- ✓
Enable autoscaling with minReplicaCount=0
Why this is correct
minReplicaCount=0 allows the endpoint to scale to zero when idle.
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.
- ✗
Set maxReplicaCount to 0
Why it's wrong here
maxReplicaCount=0 would prevent any scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse `minReplicaCount=0` with `maxReplicaCount=0`, thinking that setting the maximum to zero will scale down to zero, but in reality, `maxReplicaCount=0` disables the endpoint entirely, while `minReplicaCount=0` is the correct parameter to allow scaling to zero instances.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI endpoints use the Knative serving layer, which supports scale-to-zero by setting the `minReplicaCount` to 0, allowing the endpoint to have zero pods when idle. The endpoint's autoscaler monitors request metrics and triggers a cold start when traffic arrives, provisioning a new replica from scratch, which can introduce latency but saves costs during inactivity. In real-world scenarios, this is ideal for batch inference or sporadic traffic patterns, but you must account for cold start latency and ensure your model loads quickly to avoid timeouts.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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.
- →
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 with minReplicaCount=0 — Option C is correct because Vertex AI endpoints support autoscaling with a `minReplicaCount` of 0, which allows the endpoint to scale down to zero instances when there are no incoming requests, thereby minimizing costs. This feature is specifically designed for serverless model serving, where the endpoint automatically scales up from zero when traffic arrives and scales down to zero during idle periods.
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.
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.
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Last reviewed: Jul 4, 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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