AI Associate AI Fundamentals Practice Question
Which TWO of the following are common causes of model drift in Einstein Discovery?
⚠ Common exam trap
Salesforce often tests the distinction between factors that degrade model performance (like poor data quality or overfitting) versus the specific external or temporal changes that cause model drift, leading candidates to mistakenly select options like increased complexity or reduced dataset size.
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
✓
Seasonal patterns that affect the target variable
Seasonal patterns (Option B) cause model drift because the relationship between input features and the target variable changes predictably over time, such as higher sales during holidays. Einstein Discovery models trained on historical data may fail to generalize if the seasonal cycle is not captured or if the model is not retrained to account for these recurring shifts, leading to degraded prediction accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Improved data quality after cleaning
Why it's wrong here
Better data quality generally improves model stability, not drift.
- ✓
Seasonal patterns that affect the target variable
Why this is correct
Seasonality can introduce cyclic changes that the model may not capture if not retrained.
- ✗
Increased model complexity
Why it's wrong here
Complexity can lead to overfitting but not necessarily to drift.
- ✓
Changes in customer behavior over time
Why this is correct
Shifts in behavior alter the data distribution, causing drift.
- ✗
Reduced size of the training dataset
Why it's wrong here
Smaller dataset may increase variance but is not a direct cause of drift.
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