AI Associate AI Fundamentals Practice Question
A data analyst is reviewing an Einstein Discovery story and notices that one input feature has a very high influence on the predicted outcome. Which two conclusions are justified based on this observation? (Choose 2)
⚠ Common exam trap
Salesforce often tests the distinction between correlation and causation, and the trap here is assuming that high feature influence implies a direct causal link or that removing the feature will always improve the model.
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 could be a surrogate for other correlated features.
A feature with high influence in an Einstein Discovery story may be acting as a proxy for other correlated features, meaning its apparent importance could be due to shared variance with other predictors. This is a known phenomenon in machine learning where collinearity can inflate a feature's influence score without it being uniquely causal.
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 feature has a causal relationship with the outcome.
Why it's wrong here
Influence does not imply causation.
- ✗
Removing the feature will significantly improve model performance.
Why it's wrong here
Removal may degrade performance.
- ✗
The model is likely overfitted to that feature.
Why it's wrong here
High influence does not indicate overfitting.
- ✓
The feature could be a surrogate for other correlated features.
Why this is correct
Could represent collinear features.
- ✓
The feature is the most important predictor of the outcome.
Why this is correct
High influence implies importance.
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