easyMultiple Choice
PMLE Practice Question: A retail company wants to predict customer churn…
A retail company wants to predict customer churn using their transaction history and customer demographics. They have limited ML expertise and want to use a managed service on Google Cloud. Which service should they use?
⚠ Common exam trap
Candidates often confuse BigQuery ML as a fully managed no-code solution, but it still requires SQL proficiency and manual model selection, whereas Vertex AI AutoML is the true zero-code managed service for tabular data.
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 (Tables)
Vertex AI AutoML (Tables) is the correct choice because it is a managed service specifically designed for tabular data, requiring no ML expertise. It automates model training, hyperparameter tuning, and deployment for classification tasks like churn prediction, directly handling transaction history and demographic features.
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 Notebooks
Why it's wrong here
AI Platform Notebooks provides interactive Jupyter environments for exploratory analysis and custom model development, but it supplies no managed training, tuning or deployment pipeline — the team would still build and operate the full workflow themselves. It is tempting because notebooks suit prototyping and experimentation, and would be the right choice for iterative data exploration before committing to a production model.
- ✓
Vertex AI AutoML (Tables)
Why this is correct
Vertex AI AutoML (Tables) trains tabular classification models on transaction and demographic data without requiring coding, satisfying the limited ML expertise constraint. Its managed pipeline handles feature engineering, model selection and tuning automatically, so the retail team can deploy churn predictions without building custom infrastructure.
- ✗
Cloud TPU
Why it's wrong here
Cloud TPU provides hardware acceleration for training and serving large TensorFlow models, not a managed churn-prediction service. It tempts teams seeking Google Cloud ML compute, but Vertex AI offers managed training, AutoML and deployment suited to limited ML expertise, whereas TPUs require substantial model engineering.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML trains models using SQL inside BigQuery, but it demands SQL and ML understanding to select algorithms and tune features, conflicting with limited expertise. Vertex AI AutoML would suit low-code churn prediction, though BigQuery ML fits SQL-fluent analysts.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.