AI Associate AI Fundamentals Practice Question
A company uses an AI model to classify customer support cases into categories. The model often misclassifies cases from a specific region, leading to longer resolution times. What is the most likely cause?
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
✓
The training data lacks diversity for that region
If the training data is not representative of all regions, the model will perform poorly on underrepresented groups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is underfitted
Why it's wrong here
Underfitting leads to poor performance across all data, not a specific region.
- ✗
The model uses too many features
Why it's wrong here
Too many features can cause overfitting, but not necessarily region-specific errors.
- ✗
The model is overfitted
Why it's wrong here
Overfitting causes poor performance on new data generally, not specifically on a region.
- ✓
The training data lacks diversity for that region
Why this is correct
Machine learning models learn from data; if a region is underrepresented, the model may not learn its patterns.
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