AI Associate AI Fundamentals Practice Question
A data scientist is building a churn prediction model. What THREE factors are most critical for model success?
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
✓
Relevant features correlated with churn
Quality labeled historical data ensures the model learns from accurate patterns. Relevant features (e.g., usage frequency, support tickets) improve predictions. A representative dataset avoids bias and ensures generalization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
High number of model parameters
Why it's wrong here
More parameters can cause overfitting; not a critical success factor.
- ✓
Relevant features correlated with churn
Why this is correct
Good features drive model performance.
- ✓
Representative dataset reflecting all customer segments
Why this is correct
Representative data prevents bias and ensures generalization.
- ✓
Quality labeled historical data
Why this is correct
Accurate labels are fundamental for supervised learning.
- ✗
Large dataset size
Why it's wrong here
Size alone is not critical if data is low quality or irrelevant.
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