AI-103 Computer Vision Practice Question
You are preparing training images for a Custom Vision object detection project. Which THREE data preparation practices should you follow to ensure high model accuracy? (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
✓
Vary object angles, lighting, and scale across your training images.
Good practices for Custom Vision include providing variety in backgrounds, ensuring multiple instances of objects across different angles and lighting, and meeting minimum tag counts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vary object angles, lighting, and scale across your training images.
Why this is correct
Varying lighting, angles, and scale ensures robust real-world performance.
- ✗
Use identical background images for all training samples to maintain consistency.
Why it's wrong here
Identical backgrounds cause the model to learn the background rather than the object.
- ✓
Ensure at least 15 tagged images per tag for object detection.
Why this is correct
Custom Vision requires a minimum of 15 tagged images per tag.
- ✓
Include images with varied backgrounds for the same object class.
Why this is correct
Varied backgrounds help the model generalize and avoid overfitting to a specific background.
- ✗
Keep all training images under 100x100 pixels to speed up training.
Why it's wrong here
Low resolution destroys vital feature details needed for detection.
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Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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