AI Associate Data for AI Practice Question
Which THREE factors should be considered when selecting features for a predictive model in Salesforce?
⚠ Common exam trap
Salesforce often tests the distinction between feature selection criteria (predictive power, correlation, interpretability) and broader data management concerns (privacy, volume), leading candidates to mistakenly include compliance or data volume as direct feature selection factors.
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
✓
Correlation between features to avoid multicollinearity
Multicollinearity occurs when two or more features are highly correlated, which can destabilize model coefficients and reduce interpretability. In Salesforce's predictive models, such as those built with Einstein Discovery, correlated features can inflate variance and lead to unreliable predictions. Avoiding multicollinearity ensures that the model's feature importance estimates are trustworthy and that the model generalizes well to new data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Volume of data available for each feature
Why it's wrong here
Data volume is about sample size, not feature selection.
- ✓
Correlation between features to avoid multicollinearity
Why this is correct
Multicollinearity can harm model stability.
- ✓
Relevance of the feature to the target variable
Why this is correct
Features should be predictive.
- ✗
Compliance with data privacy regulations
Why it's wrong here
Important but not a feature selection criterion.
- ✓
Business interpretability of the feature
Why this is correct
Understandable features increase trust.
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This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.