AI-102 Implement computer vision solutions Practice Question
A manufacturing company uses Azure AI Custom Vision to classify defects on assembly line parts. The model is currently trained with 500 images per class and achieves 85% accuracy. The company needs to improve accuracy to 95% without collecting new images. Which THREE actions should they take?
⚠ Common exam trap
A common mix-up: candidates confuse adjusting the probability threshold (a post-processing step) with improving model accuracy, or mistakenly think increasing iterations always helps, when in fact Custom Vision's training is already optimized to converge automatically.
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 data augmentation (e.g., rotation, scaling)
Custom Vision's built-in data augmentation (rotation, scaling, flipping, etc.) artificially increases the diversity of the training set without requiring new images. This helps the model generalize better and reduces overfitting, directly improving accuracy toward the 95% target.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the domain to 'General'
Why it's wrong here
Domain is set before training; changing it requires retraining from scratch.
- ✓
Use data augmentation (e.g., rotation, scaling)
Why this is correct
Creates variations from existing images.
- ✗
Increase the number of training iterations
Why it's wrong here
May help but less impactful than augmentation and threshold tuning.
- ✓
Adjust the probability threshold
Why this is correct
Helps balance precision and recall.
- ✓
Add negative images (images without defects)
Why this is correct
Improves model's ability to reject non-defects.
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