- A
Enable serverless serving by selecting the appropriate serving mode.
Vertex AI offers serverless mode for CPU models that supports scale-to-zero.
- B
Use a GPU-enabled machine type for faster scaling.
Why wrong: GPU machines do not support scale-to-zero; they require at least one replica.
- C
Deploy the model using a custom container with WebSockets support.
Why wrong: WebSockets are not required for autoscaling to zero.
- D
Set `min_replica_count=0` in the endpoint deployment config.
This allows the endpoint to scale down to zero.
- E
Set `max_replica_count` to a value > 0.
A non-zero max replica count is required to allow scaling up from zero.
Question 537 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.
An ML engineer needs to deploy a model to Vertex AI for online predictions and enable autoscaling to zero when not in use. Which THREE conditions must be met? (Choose 3)
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 serverless serving by selecting the appropriate serving mode.
Option A is correct because Vertex AI offers a serverless serving mode that automatically scales resources to zero when no requests are being processed. By selecting this mode, the ML engineer enables the endpoint to scale down completely during idle periods, eliminating costs for unused infrastructure. This is distinct from standard serving, which maintains a minimum number of replicas.
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 serverless serving by selecting the appropriate serving mode.
Why this is correct
Vertex AI offers serverless mode for CPU models that supports scale-to-zero.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a GPU-enabled machine type for faster scaling.
Why it's wrong here
GPU machines do not support scale-to-zero; they require at least one replica.
- ✗
Deploy the model using a custom container with WebSockets support.
Why it's wrong here
WebSockets are not required for autoscaling to zero.
- ✓
Set `min_replica_count=0` in the endpoint deployment config.
Why this is correct
This allows the endpoint to scale down to zero.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Set `max_replica_count` to a value > 0.
Why this is correct
A non-zero max replica count is required to allow scaling up from zero.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that setting `min_replica_count=0` alone is sufficient, but candidates forget that `max_replica_count` must also be set to a positive value to allow scaling up from zero.
Detailed technical explanation
How to think about this question
Vertex AI autoscaling to zero relies on the `min_replica_count=0` parameter in the deployment config, which tells the autoscaler to reduce replicas to zero when there are no incoming requests. The `max_replica_count` must be > 0 to define the upper bound for scaling. Under the hood, Vertex AI uses a request-based autoscaler that monitors latency and request queue depth, scaling down to zero only when the endpoint is idle for a sustained period, typically after a cooldown window.
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.
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Serving and Scaling Models — study guide chapter
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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 serverless serving by selecting the appropriate serving mode. — Option A is correct because Vertex AI offers a serverless serving mode that automatically scales resources to zero when no requests are being processed. By selecting this mode, the ML engineer enables the endpoint to scale down completely during idle periods, eliminating costs for unused infrastructure. This is distinct from standard serving, which maintains a minimum number of replicas.
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.
About these practice questions
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 →
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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