Quick Answer
The answer is Vertex AI Workbench and Vertex AI Training. These two services are appropriate for building custom ML models on Vertex AI because they provide the full flexibility required for bespoke model development, from authoring custom code in Jupyter notebooks to executing distributed training jobs with frameworks like TensorFlow or PyTorch, without relying on any pre-built APIs. On the Google Professional Machine Learning Engineer exam, this distinction tests your understanding of the platform’s modular architecture: Workbench handles the interactive development and experimentation phase, while Training manages the scalable, production-grade compute for model fitting. A common trap is selecting AutoML or pre-built Vision/NLP APIs, which are designed for no-code or low-code solutions and explicitly excluded by the “not using pre-built APIs” constraint. Remember the mnemonic “Workbench to Write, Training to Train” — if you need to write custom code from scratch, you need both of these services, not the turnkey alternatives.
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 company is evaluating Google Cloud ML solutions. Which TWO services are appropriate for building custom machine learning models (not using pre-built APIs)? (Choose TWO.)
Vertex AI Workbench
Notebooks for custom model development.
Cloud Translation API
Why wrong: Pre-built API, not for custom model building.
Vertex AI Training
Custom training on Vertex AI.
Cloud AutoML
Why wrong: AutoML automates model building but is a managed service, not fully custom.
Cloud Vision API
Why wrong: Pre-built API, not for custom model building.
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 Workbench
Vertex AI Workbench is correct because it provides a Jupyter-based development environment where data scientists can write custom code, train models from scratch, and manage the entire ML workflow without relying on pre-built APIs. It supports custom containers, frameworks like TensorFlow and PyTorch, and integrates with Vertex AI Training for distributed training.
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 Workbench
Why this is correct
Notebooks for custom model development.
Related concept
Read the scenario before looking for a memorised answer.
Cloud Translation API
Why it's wrong here
Pre-built API, not for custom model building.
Vertex AI Training
Why this is correct
Custom training on Vertex AI.
Related concept
Read the scenario before looking for a memorised answer.
Cloud AutoML
Why it's wrong here
AutoML automates model building but is a managed service, not fully custom.
Cloud Vision API
Why it's wrong here
Pre-built API, not for custom model building.
Common exam traps
Google Cloud often tests the distinction between 'building custom models' and 'using pre-built APIs' — candidates mistakenly choose AutoML or pre-built APIs because they think any ML service that trains models qualifies, but the question explicitly requires building from scratch without pre-built models.
Detailed technical explanation
Vertex AI Training supports hyperparameter tuning, distributed training across TPUs and GPUs, and custom training jobs using Python packages. Under the hood, it leverages Kubernetes for orchestration and can scale to thousands of nodes, allowing you to define custom training logic in a Docker container. A real-world scenario is training a proprietary recommendation model using user behavior data, where you need full control over the architecture and loss functions.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
The correct answer is: Vertex AI Workbench — Vertex AI Workbench is correct because it provides a Jupyter-based development environment where data scientists can write custom code, train models from scratch, and manage the entire ML workflow without relying on pre-built APIs. It supports custom containers, frameworks like TensorFlow and PyTorch, and integrates with Vertex AI Training for distributed training.
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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