easyMultiple Select
PMLE Practice Question: Which TWO of the following are low-code machine…
Which TWO of the following are low-code machine learning solutions on Google Cloud?
⚠ Common exam trap
Google Cloud often tests the distinction between general-purpose ML frameworks (like TensorFlow, scikit-learn, PyTorch) that require significant coding versus managed services (BigQuery ML, AutoML) that provide low-code or no-code interfaces, leading candidates to mistakenly classify any ML tool on Google Cloud as low-code.
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
✓
BigQuery ML
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
TensorFlow
Why it's wrong here
TensorFlow is a code-first framework where you write Python to define layers, losses, and training loops, so it is not low-code. Its prominence in ML makes it an easy pick. Google Cloud's low-code offerings are Vertex AI AutoML and BigQuery ML, which train models from data and settings rather than code.
- ✗
scikit-learn
Why it's wrong here
scikit-learn is an open-source Python library requiring code to define pipelines, train models, and tune hyperparameters, so it is not low-code. Its familiarity as a standard ML toolkit makes it tempting. Vertex AI AutoML and BigQuery ML provide the low-code, configuration-driven training the question targets.
- ✗
PyTorch
Why it's wrong here
PyTorch is a code-first deep learning framework; building and training a model requires writing Python, so it is not low-code. Its popularity makes it tempting. The low-code Google Cloud solutions are Vertex AI AutoML and BigQuery ML, which generate models from configuration and data rather than imperative code.
- ✓
BigQuery ML
Why this is correct
BigQuery ML lets analysts create and train models using SQL directly inside BigQuery, with no coding or data movement. This satisfies the low-code requirement, since model training runs where the data already resides, avoiding export to separate ML environments.
- ✓
Vertex AI AutoML
Why this is correct
Vertex AI AutoML trains custom models through a point-and-click interface, requiring no coding, which directly satisfies the low-code constraint in the stem. It supports tabular, image, text and video data, generating deployable endpoints automatically. This makes it a genuine low-code machine learning solution on Google Cloud.
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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.