AI Associate Data for AI Practice Question
After applying a log transformation to a numeric feature, an Einstein model’s performance dropped significantly. What is the most likely cause?
⚠ Common exam trap
Salesforce often tests the misconception that log transformation always improves model performance, but the trap here is that candidates overlook the mathematical constraint that log is undefined for non-positive values, causing them to choose a less relevant option like data volume reduction or multicollinearity.
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 feature contained zero or negative values
Log transformation is undefined for zero or negative values because log(0) is negative infinity and log of a negative number is not a real number. In Salesforce Einstein, numeric features with such invalid transformed values can cause the model to fail or produce erratic results, leading to a significant drop in performance. This is the most likely cause given the symptom described.
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 volume was reduced by the transformation
Why it's wrong here
Transformation does not remove records.
- ✗
The feature was normally distributed after transformation
Why it's wrong here
Normality is not required; performance drop indicates a problem.
- ✓
The feature contained zero or negative values
Why this is correct
Log of non-positive values is undefined, causing missing or infinity values.
- ✗
The transformation introduced multicollinearity with other features
Why it's wrong here
Log transform is monotonic and does not cause collinearity alone.
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