AI-103 Computer Vision Practice Question
You are troubleshooting a Custom Vision object detection model that exhibits poor performance in production. Which THREE actions can you take using the Custom Vision portal or APIs to diagnose and improve the model? (Choose three.)
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
✓
Examine prediction suggestions to find low-confidence or incorrectly predicted images and add them to training data.
Portal diagnostics include reviewing prediction performance metrics (Precision, Recall, mAP), tagging missed detections (Smart Labeling / Suggested Tags), and adjusting probability thresholds.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete all project iteration history to reset model weights to random initialization.
Why it's wrong here
Deleting history destroys training state and does not solve underlying data quality issues.
- ✓
Examine prediction suggestions to find low-confidence or incorrectly predicted images and add them to training data.
Why this is correct
Adding hard examples from prediction suggestions (active learning) improves model robustness.
- ✓
Review precision and recall metrics per tag in the Performance tab.
Why this is correct
Performance metrics highlight which tags are underperforming.
- ✓
Adjust the probability threshold on the performance page to evaluate precision/recall trade-offs.
Why this is correct
Threshold adjustment helps determine the optimal balance for production usage.
- ✗
Manually edit model neural network weights via the REST API.
Why it's wrong here
Custom Vision does not support manual weight editing.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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