The answer is that a feature with very high influence in an Einstein Discovery story is the most important predictor of the outcome, but this observation must be interpreted with caution because the feature may be acting as a proxy for other correlated predictors. This technical concept, known as collinearity, can inflate a feature’s influence score by sharing variance with other inputs, making it appear uniquely powerful when it is not. On the Salesforce AI Associate exam, this tests your understanding of how to critically evaluate feature influence in stories, a common trap being to assume high influence always means direct causation. A useful memory tip is “high influence, high suspicion”—when you see a single feature dominate, always check for hidden correlations before drawing conclusions.
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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)
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
The feature is the most important predictor of the outcome.
Why this is correct
High influence implies importance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
In Einstein Discovery, feature influence is computed using Shapley values or permutation importance, which measure the average marginal contribution of a feature to the prediction. When features are correlated, the Shapley value can distribute importance among them, making one feature appear highly influential even if it is just a surrogate for others. In practice, this means analysts should use correlation matrices or variance inflation factors (VIF) to check for multicollinearity before interpreting feature importance.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The feature could be a surrogate for other correlated features. — Option D is correct because 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.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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