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 negatives improves precision, not recall.
- ✗
Increase the number of training iterations
Why it's wrong here
More iterations may not specifically improve recall.
- ✓
Lower the probability threshold for the defect class
Why this is correct
Lowering the threshold increases the number of positive predictions, thus improving recall.
- ✗
Increase the probability threshold for the defect class
Why it's wrong here
Increasing the threshold reduces false positives but also reduces recall.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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