The answer is Vertex AI Endpoints, the best deployment option to deploy a TensorFlow model with low latency on Google Cloud. This is correct because Vertex AI Endpoints are purpose-built for online predictions, providing a managed serving infrastructure that leverages TensorFlow Serving, automatic scaling, and hardware accelerators like GPUs or TPUs to consistently achieve sub-100ms response times. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of deployment trade-offs, often trapping candidates who might choose Cloud Run or AI Platform Prediction—but Vertex AI Endpoints offer integrated monitoring and optimization for latency-sensitive workloads. A key memory tip is to associate "Endpoints" with "low-latency online serving" and remember that batch predictions or Cloud Functions lack the dedicated, optimized runtime needed for real-time inference.
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. 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 data scientist trained a custom TensorFlow model using Vertex AI Training and wants to deploy it for online predictions with low latency (<100ms). Which deployment option on Google Cloud is best?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy on Vertex AI Endpoints
Vertex AI Endpoints is the correct choice because it is purpose-built for deploying TensorFlow models with optimized serving infrastructure, including automatic scaling, GPU/TPU support, and built-in monitoring for latency-sensitive online predictions. It provides a managed endpoint that can achieve sub-100ms latency by leveraging model optimization techniques like TensorFlow Serving and hardware accelerators, which are not available in the other options.
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 on Cloud Run with a custom container
Why it's wrong here
Cloud Run may have cold starts and is not optimized for ML inference.
✗
Deploy on Cloud Functions
Why it's wrong here
Cloud Functions have limited resources and timeouts, unsuitable for ML.
✗
Deploy on AI Platform Prediction (legacy)
Why it's wrong here
AI Platform Prediction is deprecated in favor of Vertex AI.
✓
Deploy on Vertex AI Endpoints
Why this is correct
Vertex AI Endpoints provide managed, scalable, low-latency online prediction.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any serverless option (like Cloud Run or Cloud Functions) is sufficient for low-latency ML inference, ignoring the need for GPU acceleration and optimized serving infrastructure that only Vertex AI Endpoints provides.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints use TensorFlow Serving under the hood, which supports batching and model versioning to optimize throughput and latency. The service can automatically scale to zero when not in use, but for low-latency requirements, it is recommended to keep a minimum number of instances warm to avoid cold starts. In real-world scenarios, a model requiring GPU inference (e.g., a large NLP model) would benefit from Vertex AI Endpoints' ability to attach NVIDIA GPUs like T4 or V100, which Cloud Run and Cloud Functions cannot provide.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy on Vertex AI Endpoints — Vertex AI Endpoints is the correct choice because it is purpose-built for deploying TensorFlow models with optimized serving infrastructure, including automatic scaling, GPU/TPU support, and built-in monitoring for latency-sensitive online predictions. It provides a managed endpoint that can achieve sub-100ms latency by leveraging model optimization techniques like TensorFlow Serving and hardware accelerators, which are not available in the other options.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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