AI-102 Plan and manage an Azure AI solution Practice Question
You are responsible for an Azure AI solution that uses Custom Vision to classify manufacturing defects. The model must achieve high recall to avoid missing defects. The current model has high precision but low recall. Which action should you take?
⚠ Common exam trap
Candidates often confuse precision and recall, often assuming that increasing the threshold (making the model stricter) will improve overall performance, when in fact it reduces recall by missing more defects.
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
✓
Lower the probability threshold for the defect class
Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more images of non-defective items to the training set
Why it's wrong here
Adding non-defective images enlarges the negative class, biasing the model toward predicting 'no defect' and lowering recall. It is tempting because more training data usually helps, and would be correct if the problem were false positives from insufficient negative examples.
- ✗
Increase the number of training iterations
Why it's wrong here
More iterations tune the existing decision boundary; they do not shift it toward catching positives, so recall need not improve. It is tempting because extra training often raises overall accuracy, and would be correct if the model were underfitted rather than biased toward precision over recall.
- ✓
Lower the probability threshold for the defect class
Why this is correct
Lowering the probability threshold classifies more samples as defects, raising recall by catching defects previously missed, at the cost of some precision. This directly addresses the high-precision, low-recall imbalance, prioritising detection of defects over avoiding false positives.
- ✗
Increase the probability threshold for the defect class
Why it's wrong here
Raising the threshold makes the model stricter about labelling defects, which reduces recall further by converting borderline positives to negatives. It is tempting because thresholds control precision/recall balance, and would be correct if the goal were higher precision or fewer false positives.
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