AI-102 Implement computer vision solutions Practice Question
A company uses Azure Custom Vision to build a classifier for defect detection on a manufacturing line. They have labeled images of products with and without defects. Which TWO actions should they take to improve model performance?
⚠ Common exam trap
A common mix-up: candidates think reducing images prevents overfitting (Option E) or that manual learning rate tuning (Option C) is possible in Custom Vision, but the service abstracts hyperparameter tuning and requires sufficient, varied data for robust defect detection.
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 images with balanced numbers of defect and non-defect samples.
Balanced datasets prevent the model from becoming biased toward the majority class (e.g., non-defect images), which is critical for defect detection where defects are rare. Azure Custom Vision uses a weighted loss function during training, and class imbalance can cause the model to predict the majority class for most inputs, reducing recall for defects. Balanced samples ensure the model learns discriminative features for both classes equally.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train for more iterations without validation.
Why it's wrong here
No validation increases risk of overfitting.
- ✓
Use images with balanced numbers of defect and non-defect samples.
Why this is correct
Balanced datasets prevent bias toward majority class.
- ✗
Set the learning rate manually using the Custom Vision API.
Why it's wrong here
Custom Vision does not expose learning rate for manual tuning.
- ✓
Increase the number of images per tag, including variations in lighting and angle.
Why this is correct
More diverse data improves robustness.
- ✗
Reduce the number of images per tag to avoid overfitting.
Why it's wrong here
Reducing data harms generalization.
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