AI-102 Implement computer vision solutions Practice Question
A retail company uses Azure AI Vision to analyze store shelf images for product availability. The solution uses an object detection model trained on custom products. Recently, the model's performance dropped significantly due to new packaging designs. You need to improve the model's accuracy with minimal manual effort. What should you do?
⚠ Common exam trap
Test-takers frequently assume retraining from scratch (Option D) is the only way to fix model drift, underestimating the power of active learning to efficiently handle distribution shifts with minimal manual effort.
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 Azure AI Vision model customization with active learning
Azure AI Vision model customization with active learning is the correct choice because it allows the model to automatically identify images where it is uncertain (low confidence predictions) and prioritize those for labeling and retraining. This minimizes manual effort while directly addressing the performance drop caused by new packaging designs, as the model iteratively improves on the specific data distribution shift without requiring a full retraining from scratch.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Azure AI Vision model customization with active learning
Why this is correct
Active learning selects the most informative images for labeling, minimizing manual effort.
- ✗
Adjust the confidence score threshold to reduce false negatives
Why it's wrong here
Does not improve model recognition of new packaging.
- ✗
Use the Image Analysis 4.0 dense captioning feature
Why it's wrong here
Provides captions, not object detection for custom products.
- ✗
Collect a new set of images and retrain the model from scratch
Why it's wrong here
Requires full manual labeling effort.
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