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.
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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 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?
Kubeflow Pipelines
Why wrong: Kubeflow Pipelines is for orchestrating ML workflows, not for low-code model training.
Custom TensorFlow model
Why wrong: Requires expertise in TensorFlow and model development.
BigQuery ML
Why wrong: BigQuery ML requires SQL and understanding of ML concepts; it's not fully low-code.
Vertex AI AutoML Tables
AutoML Tables provides automated model training and deployment without requiring deep ML knowledge.
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 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.
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.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Vertex AI AutoML Tables uses neural architecture search and transfer learning to automatically find the best model architecture for tabular data, handling missing values, categorical encoding, and feature crosses under the hood. It also provides feature importance analysis and model explainability via Shapley values, which is critical for business stakeholders to trust churn predictions. In a real-world scenario, a non-technical analyst could upload a CSV of customer data, select the churn column as the target, and get a deployable model in hours without any ML pipeline code.
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
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
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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Same concept, more angles
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Variation 1. Which TWO of the following are low-code machine learning solutions on Google Cloud?
easyWhy 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.
Last reviewed: Jun 30, 2026
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