AI Associate AI Fundamentals Practice Question
A company uses computer vision to scan receipts for expense reporting. The model performs well on high-resolution scans but poorly on blurry photos. Which improvement is most effective?
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
✓
Add blurry images to the training data
Augmenting training data with blurry images helps the model learn to handle various quality levels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the learning rate
Why it's wrong here
Learning rate affects convergence, not handling of blurry inputs.
- ✓
Add blurry images to the training data
Why this is correct
Training on blurry examples teaches the model to handle that variation.
- ✗
Use a larger batch size
Why it's wrong here
Batch size affects training stability, not robustness to blur.
- ✗
Increase the model's number of layers
Why it's wrong here
Adding layers may increase capacity but does not specifically address blur.
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