AI-102 Implement computer vision solutions Practice Question
You are deploying a custom image classification model using Azure AI Custom Vision. The model must achieve high accuracy on a dataset with subtle differences between classes. However, the training set is small (200 images per class). Which strategy should you use to improve model performance?
⚠ Common exam trap
Candidates often assume more data or longer training (options A, B, C) can overcome a small dataset, but Azure Custom Vision's design explicitly relies on transfer learning to achieve high accuracy with limited samples.
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 transfer learning with a pre-trained model and fine-tune
Transfer learning with a pre-trained model (e.g., ResNet, EfficientNet) is ideal for small datasets because it leverages features learned from large-scale datasets like ImageNet. Fine-tuning adjusts only the final layers to the new task, preventing overfitting and achieving high accuracy even with subtle inter-class differences. Custom Vision automatically uses transfer learning, making option D the correct strategy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable hyperparameter tuning and increase the number of iterations
Why it's wrong here
Tuning helps but doesn't address data scarcity as effectively as transfer learning.
- ✗
Apply aggressive data augmentation and train from scratch
Why it's wrong here
Training from scratch on small data often underperforms compared to transfer learning.
- ✗
Use a larger batch size and train for more epochs
Why it's wrong here
Without more data or transfer learning, this may lead to overfitting.
- ✓
Use transfer learning with a pre-trained model and fine-tune
Why this is correct
Transfer learning leverages pre-trained weights and is effective with small datasets.
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