AI Associate AI Fundamentals Practice Question
A model trained on CRM data predicts customer lifetime value (CLV) with high accuracy, but when deployed, predictions are significantly off for new customer segments. 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 was not representative of the new segments
If the training data was not representative of the new segments, the model will not generalize, leading to poor out-of-sample performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature engineering was insufficient for the original segments
Why it's wrong here
Insufficient features would likely cause poor performance across all segments.
- ✗
The model is overfitting to the training data
Why it's wrong here
Overfitting would affect all new data, not just specific segments, and would likely show lower overall accuracy.
- ✗
The model architecture is too simple
Why it's wrong here
A simple model might underfit generally, not just on new segments.
- ✓
The training data was not representative of the new segments
Why this is correct
Unrepresentative training data leads to poor generalization for unseen segments.
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