- A
Cloud Functions
Why wrong: Cloud Functions is event-driven and not designed for continuous prediction serving with autoscaling based on request load.
- B
Batch Prediction on Vertex AI
Why wrong: Batch prediction is for asynchronous, large-scale predictions, not real-time low-latency serving.
- C
Cloud AI Platform Prediction with custom containers
Why wrong: Cloud AI Platform Prediction is an older service; Vertex AI Endpoints is the recommended and more managed solution.
- D
Vertex AI Endpoints
Vertex AI Endpoints provides managed online prediction with automatic scaling and low latency.
Question 702 of 1,000
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning 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.
A startup is deploying a machine learning model for real-time fraud detection. They need low latency and automatic scaling during peak hours. Which Google Cloud service should they use?
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
✓
Vertex AI Endpoints
Vertex AI Endpoints provide managed, autoscaling infrastructure designed for low-latency online predictions, making them ideal for real-time fraud detection. They automatically scale the number of compute nodes based on incoming traffic, ensuring peak-hour demand is met without manual intervention.
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.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions is event-driven and not designed for continuous prediction serving with autoscaling based on request load.
- ✗
Batch Prediction on Vertex AI
Why it's wrong here
Batch prediction is for asynchronous, large-scale predictions, not real-time low-latency serving.
- ✗
Cloud AI Platform Prediction with custom containers
Why it's wrong here
Cloud AI Platform Prediction is an older service; Vertex AI Endpoints is the recommended and more managed solution.
- ✓
Vertex AI Endpoints
Why this is correct
Vertex AI Endpoints provides managed online prediction with automatic scaling and low latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Cloud Functions or Batch Prediction for real-time serving, overlooking that Vertex AI Endpoints are the only option purpose-built for low-latency, autoscaling online predictions in the modern Vertex AI ecosystem.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints use a managed infrastructure that provisions and scales compute resources (e.g., CPUs, GPUs) based on the prediction request rate, leveraging Google Cloud's autoscaler with configurable min/max nodes and target utilization. Under the hood, the endpoint routes requests through a load-balanced, multi-zone deployment to minimize latency, and it supports model versioning and traffic splitting for canary deployments. In a real-world fraud detection system, this ensures sub-100ms response times even during flash sales or high-traffic events, while automatically scaling down to reduce costs during off-peak hours.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Endpoints — Vertex AI Endpoints provide managed, autoscaling infrastructure designed for low-latency online predictions, making them ideal for real-time fraud detection. They automatically scale the number of compute nodes based on incoming traffic, ensuring peak-hour demand is met without manual intervention.
What should I do if I get this PDE 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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Last reviewed: Jul 4, 2026
This PDE 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 PDE exam.
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