AI-102 Implement computer vision solutions Practice Question
You are a data scientist at a healthcare startup. You have deployed a custom object detection model using Azure Custom Vision to detect tumors in MRI scans. The model was trained on 10,000 labeled scans from a single hospital. After deployment, the model performs well on scans from that hospital but poorly on scans from a different hospital with a different MRI machine. The new hospital's scans have slightly different contrast and resolution. The model's precision drops from 0.92 to 0.65, and recall drops from 0.88 to 0.50. You have access to 500 labeled scans from the new hospital. You need to improve the model's performance on the new hospital's data as quickly as possible with minimal effort. What should you do?
⚠ Common exam trap
The trap here is that candidates may overestimate the need for large datasets or manual preprocessing, failing to recognize that Azure Custom Vision's built-in transfer learning is designed to efficiently adapt models with minimal new data.
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 the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.
Azure Custom Vision supports transfer learning, allowing you to take an existing trained model and retrain it with new labeled data. By using the 500 labeled scans from the new hospital as a training set, you can fine-tune the model to adapt to the different contrast and resolution characteristics without starting from scratch. This approach is the fastest and requires minimal effort, leveraging the previously learned features while incorporating domain-specific adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more labeled scans from the new hospital and train a new model from scratch.
Why it's wrong here
Training from scratch discards the 10,000-scan feature representation and needs far more than 500 new images to converge, which is neither quick nor low-effort. It is tempting because more data usually helps, and would be correct if the new hospital's imaging modality differed fundamentally rather than sharing the same tumour morphology.
- ✗
Create a new Custom Vision project and train only on the 500 new scans.
Why it's wrong here
Training solely on 500 new scans discards the 10,000 original images, so the model loses the features it already learned and overfits to a small sample. It is tempting because retraining from scratch feels clean, and would suit a genuinely unrelated domain where old data is irrelevant.
- ✗
Apply image preprocessing to normalize the new hospital's scans to match the old hospital's style, then use the existing model.
Why it's wrong here
Contrast and resolution shifts alter feature distributions, so normalising pixels cannot recover the tumour-detection features the model never learned from the new scanner. It is tempting because preprocessing needs no retraining and is fast, and would be correct if the mismatch were purely cosmetic rather than a domain shift.
- ✓
Use the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.
Why this is correct
Domain shift from differing contrast and resolution causes the precision and recall drop. Retraining the existing Custom Vision model with the 500 labelled new-hospital scans performs transfer learning, adapting features to the new distribution quickly and with minimal effort.
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