AI-102 Implement computer vision solutions Practice Question
Which THREE factors are critical to consider when designing a custom vision solution for a manufacturing quality inspection system?
⚠ Common exam trap
It's easy for candidates to confuse peripheral requirements (like OCR or multilingual labels) with core design factors that directly impact model accuracy, latency, and robustness in a production vision system.
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
✓
Imbalance between defective and non-defective product samples.
Class imbalance is a critical factor in custom vision solutions for manufacturing quality inspection. If defective samples are rare compared to non-defective ones, the model may become biased toward predicting the majority class, leading to poor recall for defects. Azure Custom Vision allows adjusting the probability threshold and using techniques like oversampling or weighted loss to mitigate this, but the imbalance must be accounted for during dataset preparation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Imbalance between defective and non-defective product samples.
Why this is correct
Class imbalance leads to biased models.
- ✓
Variation in lighting conditions across different inspection stations.
Why this is correct
Different lighting can degrade model performance.
- ✓
Inference latency requirements for real-time decisions.
Why this is correct
Real-time inspection requires low latency.
- ✗
The need for optical character recognition (OCR) of product serial numbers.
Why it's wrong here
OCR may be separate, not core to quality inspection.
- ✗
Multilingual support for labeling.
Why it's wrong here
Labels are internal, not a critical factor.
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