PDE Preparing and Using Data for Analysis Practice Question
A company needs to predict whether a product image contains a specific defect. They have 10,000 labeled images and want to build a model quickly without writing custom code or training from scratch. Which GCP service should they use?
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
✓
AutoML Vision
AutoML Vision is designed for custom image classification tasks with minimal ML expertise. It uses transfer learning and supports up to millions of images. AutoML Tables handles tabular data, not images. Vertex AI custom training would require more effort. AutoML NLP is for text data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AutoML Tables
Why it's wrong here
AutoML Tables builds models on structured, tabular data such as CSV rows and numeric or categorical columns, so it cannot ingest image pixels for defect classification. It is tempting because it delivers exactly the no-code, train-from-scratch-free workflow the scenario requests, but only when the dataset is tabular rather than image-based.
- ✓
AutoML Vision
Why this is correct
AutoML Vision trains image classification models from labelled datasets using Google's managed infrastructure, requiring no custom code or algorithm design. It accepts the 10,000 labelled images and handles training automatically, satisfying the constraint of building a defect-detection model quickly without training from scratch.
- ✗
Vertex AI custom training
Why it's wrong here
Vertex AI custom training requires writing training code and selecting architectures, contradicting the stated no-code, no-training-from-scratch constraint. It is tempting because it offers full control over model design, and would be correct where bespoke architectures, custom loss functions or tuning beyond managed AutoML limits are genuinely needed.
- ✗
AutoML Natural Language
Why it's wrong here
AutoML Natural Language classifies and extracts entities from text, so it cannot process image pixels to detect visual defects. It is tempting because it matches the no-code, custom-model requirement, and would be the right choice for categorising support tickets, reviews or documents instead of product photographs.
Go deeper
Related to this question
About these practice questions
One of 747 original PDE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.