Objective 2.2 Explain Data Collection/transformation Process in ML Workflow (transformations Include Standardization; Normalization; Log, Square-Root, and Logit Transformations) AIP-210 Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A data analyst is building a linear regression model to predict house prices. The feature 'lot_size' has a heavily right-skewed distribution, with most values between 2,000 and 10,000 square feet but a few extreme values above 100,000 square feet. The analyst wants to reduce the influence of these extreme values while keeping the feature interpretable. Which transformation should be applied?

   • ALogit transformation
   • BMin-max normalization
   • CStandardization (z-score)
   • DLog transformation
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A data scientist is building a support vector machine (SVM) with an RBF kernel. The features include 'salary' (range $30,000–$500,000) and 'age' (range 20–70). The scientist is concerned that the salary feature will dominate the kernel computation. Which transformation should be applied to both features to ensure the RBF kernel treats them fairly?

   • AApply a log transformation to salary only, leaving age unchanged.
   • BApply standardization to both features.
   • CApply a square-root transformation to both features.
   • DApply min-max normalization to both features.
   Reveal Solution Discussion Select an answer first
4. 13foundation · easy

   What is the typical output range of min-max normalization?

   • A[-1, 1]
   • B[-∞, +∞]
   • C[0, 1]
   • D[0, 100]
   Reveal Solution Discussion Select an answer first
5. 14expert · hard

   A data scientist is working with a feature 'click_rate' that represents the proportion of users who clicked on an ad, ranging from 0.01 to 0.99. The scientist wants to use this feature in a linear regression model. The scientist is considering a logit transformation versus a log transformation. Which consideration is most important when choosing between these two?

   • ALogit transformation is preferred when the proportions are close to 0 or 1, because it stretches the tails.
   • BLog transformation is preferred when the proportions are close to 0 or 1, because it compresses the tails.
   • CLogit transformation maps the proportion to the entire real number line, which is appropriate for linear regression.
   • DLog transformation maps the proportion to the entire real number line, which is appropriate for linear regression.
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   A feature has a heavily right-skewed distribution and contains only positive values. Which transformation is most appropriate to reduce the skewness?

   • AStandardization
   • BMin-max normalization
   • CLogit transformation
   • DLog transformation
   Reveal Solution Discussion Select an answer first