This PMLE practice question tests your understanding of scaling prototypes into ml 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 team has a trained TensorFlow model running locally and wants to deploy it for low-latency online predictions on Google Cloud. Which service should they use?
Vertex AI Prediction
Vertex AI Prediction is purpose-built for low-latency online ML predictions.
AI Platform Training
Why wrong: AI Platform Training is for training jobs, not serving predictions.
Cloud Run
Why wrong: Cloud Run can serve models but is less optimized for ML than Vertex AI Prediction.
Cloud Functions
Why wrong: Cloud Functions is for event-driven functions, not ideal for ML prediction latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Vertex AI Prediction
Vertex AI Prediction is the correct choice because it is a fully managed service designed specifically for deploying trained ML models for online (real-time) prediction with low latency. It supports importing TensorFlow SavedModel artifacts and automatically scales the serving infrastructure, including GPU/TPU support, to handle request traffic while providing built-in monitoring and explainability features.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Vertex AI Prediction
Why this is correct
Vertex AI Prediction is purpose-built for low-latency online ML predictions.
Related concept
Read the scenario before looking for a memorised answer.
AI Platform Training
Why it's wrong here
AI Platform Training is for training jobs, not serving predictions.
Cloud Run
Why it's wrong here
Cloud Run can serve models but is less optimized for ML than Vertex AI Prediction.
Cloud Functions
Why it's wrong here
Cloud Functions is for event-driven functions, not ideal for ML prediction latency.
Common exam traps
Google Cloud often tests the distinction between training and prediction services, and the trap here is that candidates may confuse AI Platform Training (which is for model training) with AI Platform Prediction (now part of Vertex AI), or assume that any serverless compute like Cloud Run or Cloud Functions can handle ML inference without considering the need for GPU/TPU support and optimized serving infrastructure.
Detailed technical explanation
Vertex AI Prediction uses a model server built on TensorFlow Serving under the hood, which supports gRPC and REST endpoints for high-performance inference. It automatically handles model versioning, traffic splitting for A/B testing, and can scale to zero when not in use, while also integrating with Vertex AI Model Registry for lifecycle management. In a real-world scenario, a team deploying a real-time fraud detection model would benefit from Vertex AI Prediction's ability to serve predictions in under 100ms with autoscaling based on request load, which is not achievable with Cloud Functions or Cloud Run without significant custom engineering.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
The correct answer is: Vertex AI Prediction — Vertex AI Prediction is the correct choice because it is a fully managed service designed specifically for deploying trained ML models for online (real-time) prediction with low latency. It supports importing TensorFlow SavedModel artifacts and automatically scales the serving infrastructure, including GPU/TPU support, to handle request traffic while providing built-in monitoring and explainability features.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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