AI Associate AI Fundamentals Practice Question
A sales team notices that their lead scoring model assigns high scores to leads that rarely convert. The model was trained on data from the past 5 years. 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
✓
There is concept drift because buying behaviors have changed
Concept drift occurs when the relationship between features and labels changes over time. Old data may no longer represent current sales patterns.
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 features used are irrelevant to lead conversion
Why it's wrong here
If features were irrelevant, the model would never have performed well; the problem emerged over time.
- ✗
The model is overfitting to noise in the training data
Why it's wrong here
Overfitting would cause poor performance on new data, but the specific issue here is that old patterns are no longer valid.
- ✗
The model is underfitting and needs more features
Why it's wrong here
Underfitting would show poor performance on both training and new data; the issue is specifically with time relevance.
- ✓
There is concept drift because buying behaviors have changed
Why this is correct
Correct. Changes in market conditions or buyer behavior cause old data to become less predictive.
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