PMLE Architecting Low-Code ML Solutions Practice Question
A marketing team wants to build a model that predicts customer lifetime value (CLV) using historical transaction data. They are comfortable with spreadsheets but have no coding experience. They need a low-code solution that automatically handles feature engineering and model selection. Which Google Cloud service should they use?
⚠ Common exam trap
The trap here is assuming that BigQuery ML is a no-code solution because it uses SQL, but it still requires query writing and manual model configuration.
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 AutoML
Vertex AI AutoML is designed for users with limited ML expertise to build models without writing code. It automates feature engineering, model selection, and hyperparameter tuning. For predicting CLV from historical data, the marketing team can simply upload a dataset and let AutoML train a regression model. Other options require SQL or Python coding, which the team lacks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AI Platform Training
Why it's wrong here
AI Platform Training (now part of Vertex AI) is a service for running custom training jobs, requiring users to package their code and specify machine types. It is not low-code and does not automate feature engineering or model selection. The marketing team would need to write and manage training code, which is beyond their skill set.
- ✓
Vertex AI AutoML
Why this is correct
Vertex AI AutoML provides a fully managed, no-code environment where users can upload data and automatically train models with feature engineering and hyperparameter tuning handled by the service. It supports tabular data for regression tasks like predicting CLV. The marketing team can use the UI without writing code, making it the ideal low-code solution.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML requires writing SQL queries to create and train models. While it is low-code compared to programming, it still demands SQL knowledge. The marketing team lacks coding experience, and BigQuery ML does not automatically perform feature engineering or model selection; users must manually specify the model type and features. Thus, it is not the best fit for a no-code solution.
- ✗
Vertex AI Workbench
Why it's wrong here
Vertex AI Workbench is a managed Jupyter notebook environment that requires coding in Python or other languages to build and train models. It is not a low-code solution and offers no automated feature engineering or model selection. The marketing team with no coding experience would struggle to use it effectively for predicting CLV.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.