AI-102 Implement computer vision solutions Practice Question
You are developing a solution to detect defects on a manufacturing assembly line using computer vision. The solution must classify images as 'defective' or 'non-defective'. You have a limited set of labeled images (500 per class). Which approach should you recommend?
⚠ Common exam trap
Test-takers frequently confuse image classification (assigning a single label to the whole image) with object detection (locating objects), or assume that a pre-built model can be retrained for custom classes, when in fact Azure AI Custom Vision is the correct service for custom classification with limited data.
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
✓
Use Azure AI Custom Vision with image classification
Azure AI Custom Vision with image classification is the best choice because it allows you to fine-tune a pre-trained deep learning model on your limited dataset (500 images per class) to classify images as 'defective' or 'non-defective'. This approach requires minimal data and expertise compared to training from scratch, and it is specifically designed for custom classification tasks with small datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure AI Vision Image Analysis with a pre-built model
Why it's wrong here
Pre-built models are not tailored to specific defect categories and would not perform well with only two custom classes.
- ✓
Use Azure AI Custom Vision with image classification
Why this is correct
Custom Vision with transfer learning is ideal for small datasets and binary classification tasks like defect detection.
- ✗
Use Azure AI Custom Vision with object detection
Why it's wrong here
Object detection is for locating objects in an image, not binary classification of the whole image.
- ✗
Train a deep learning model from scratch using Azure Machine Learning
Why it's wrong here
Training from scratch requires a large dataset (typically thousands of images per class) and is not efficient with only 500 images per class.
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