easyMultiple ChoiceObjective-mapped
Low-Code ML for Tabular Data: Churn Prediction
A company wants to predict customer churn using a dataset with 10,000 rows and 20 features. They have no ML expertise. Which low-code solution should they use?
Quick Answer
The answer is Vertex AI AutoML Tables. This is the correct low-code solution because it is specifically designed for tabular data like the 10,000-row, 20-feature churn dataset, automating the entire ML pipeline—feature engineering, model selection, and hyperparameter tuning—through a simple UI, requiring zero code or ML expertise. On the Google Professional Machine Learning Engineer exam, this scenario tests your ability to match the right tool to the user’s skill level and data type, often appearing as a trap where candidates might choose BigQuery ML (which requires SQL) or pre-built APIs (which don’t handle custom tabular predictions). The key distinction is that AutoML Tables is purpose-built for structured, low-code churn prediction, while other options demand coding or are for unstructured data. Memory tip: think “AutoML Tables for tabular trouble” to recall that structured data with no-code needs points directly to this service.
⚠ Common exam trap
Google Cloud often tests the distinction between low-code/no-code solutions (like AutoML Tables) and platforms that still require coding or infrastructure expertise (like Kubeflow or custom TensorFlow), leading candidates to pick a technically capable but overly complex option.
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 low-code solution because it allows users with no ML expertise to train high-quality tabular models on structured data (10,000 rows, 20 features) without writing any code. It automates feature engineering, model selection, and hyperparameter tuning, and provides a simple UI to upload data and get predictions. This directly matches the requirement of a low-code, no-expertise solution for a tabular churn prediction problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Kubeflow Pipelines
Why it's wrong here
Kubeflow Pipelines is for orchestrating ML workflows, not for low-code model training.
- ✗
Custom TensorFlow model
Why it's wrong here
Requires expertise in TensorFlow and model development.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML requires SQL and understanding of ML concepts; it's not fully low-code.
- ✓
Vertex AI AutoML Tables
Why this is correct
AutoML Tables provides automated model training and deployment without requiring deep ML knowledge.
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Variation 1. Which TWO of the following are low-code machine learning solutions on Google Cloud?
easy- A.TensorFlow
- B.scikit-learn
- C.PyTorch
- ✓ D.BigQuery ML
- ✓ E.Vertex AI AutoML
Why D: BigQuery ML (D) is a low-code ML solution because it allows users to create, train, and deploy machine learning models using standard SQL queries directly within BigQuery, eliminating the need for custom coding in Python or other programming languages. Vertex AI AutoML (E) is also low-code as it provides a graphical interface and automated pipeline to train high-quality models with minimal manual intervention, handling feature engineering, model selection, and hyperparameter tuning automatically.
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.