PMLE Architecting Low-Code ML Solutions Practice Question
A data scientist wants to use AutoML to classify images of retail products into categories. There are 50 categories and the dataset has 100,000 labelled images. Which Vertex AI AutoML service is most appropriate?
⚠ Common exam trap
PMLE often tests whether candidates match the data modality (image vs. tabular vs. text vs. video) to the correct AutoML service, so choosing AutoML Tables for image data is the common error.
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 Vertex AI's service for training custom image classification and object detection models. With 100,000 labeled images across 50 categories, AutoML Vision can train a multi-class image classifier that learns visual features for each product category. It is the appropriate choice when the input data is images and the task is classification.
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, so it cannot ingest image pixels for classification. It is tempting because the dataset is large and labelled, which suits tabular training, and it would be correct for predicting from CSV or BigQuery columns such as price or category attributes.
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AutoML Video
Why it's wrong here
AutoML Video classifies or tracks content across video frames, not static product photographs. It is tempting because video models also perform visual classification, and it would be correct if the retail products were recorded in video clips rather than supplied as individual images.
- ✓
AutoML Vision
Why this is correct
AutoML Vision handles multi-class image classification directly, supporting up to 50 categories without custom architecture work. It ingests the 100,000 labelled images and trains a managed model, satisfying the stem's classification requirement. Vertex AI's image data type is the appropriate task-specific service here, unlike tabular or video alternatives.
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AutoML NLP
Why it's wrong here
AutoML NLP processes text for classification, entity extraction or sentiment, and cannot accept image inputs. It is tempting because 50 category labels resemble a text classification task, and it would be correct if product descriptions or reviews were the training data instead of images.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.