AI Associate Data for AI Practice Question
A data scientist is preparing numeric features for a regression model. Which TWO transformations are commonly applied to improve model performance?
⚠ Common exam trap
Salesforce often tests the distinction between data cleaning (e.g., outlier removal) and feature transformation (e.g., scaling), leading candidates to mistakenly select outlier removal as a transformation that improves model performance.
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
✓
Normalize to a 0-1 range
Normalizing features to a 0-1 range (min-max scaling) ensures that all numeric features contribute equally to the model, preventing features with larger magnitudes from dominating the gradient descent optimization. This is especially important for distance-based algorithms like k-nearest neighbors or neural networks, where feature scale directly impacts convergence speed and model 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.
- ✓
Normalize to a 0-1 range
Why this is correct
Scales features to a common range, helpful for distance-based models.
- ✗
Remove outliers beyond 3 standard deviations
Why it's wrong here
Outliers may contain signal; removal should be justified, not automatic.
- ✗
Convert numbers to string labels
Why it's wrong here
Loses order and magnitude, degrading performance.
- ✗
Apply one-hot encoding
Why it's wrong here
One-hot encoding is for categorical features, not numeric.
- ✓
Standardize to mean 0 and variance 1
Why this is correct
Centers data and scales variance, useful for linear models.
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