AI Associate Data for AI Practice Question
A company is using Einstein Discovery to predict customer churn. The model was created six months ago and has been making predictions. Recently, the model's accuracy has dropped significantly. The data scientist confirms that the data schema has not changed. What is the most likely reason for the drop in accuracy?
⚠ Common exam trap
Salesforce often tests the distinction between data schema changes (which would affect feature availability) and concept drift (which affects the relationship between features and the target), leading candidates to incorrectly choose options about data freshness or feature relevance when the real issue is a shift in the underlying data distribution.
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 model is suffering from concept drift
Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate even though the data schema remains unchanged. In Einstein Discovery, models are trained on historical data, and if the underlying patterns of customer churn evolve (e.g., due to market shifts or new competitor behavior), the model's learned relationships become stale. Since the data schema is confirmed unchanged, concept drift is the most likely cause of the accuracy drop.
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 data source is not being refreshed daily
Why it's wrong here
Data freshness is separate from concept drift; even fresh data can change patterns.
- ✗
The model's features have become irrelevant
Why it's wrong here
The data schema hasn't changed, so features are still present; their relevance may have changed due to drift.
- ✓
The model is suffering from concept drift
Why this is correct
Concept drift happens when the statistical properties of the target variable change over time.
- ✗
The model needs to be retrained weekly instead of monthly
Why it's wrong here
Retraining frequency might help, but the root cause is concept drift.
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