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 4)

Questions 16–20

1. 16application · medium

   A data scientist is preparing features for a principal component analysis (PCA). The dataset contains 'height_cm' (mean 170, std 10) and 'weight_kg' (mean 70, std 15). The scientist wants to ensure that both features contribute equally to the principal components. Which transformation should be applied to both features before PCA?

   • ASquare-root transformation
   • BStandardization (z-score)
   • CMin-max normalization
   • DLog transformation
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   What is the primary purpose of applying a square-root transformation to a feature?

   • ATo stabilize variance and reduce skewness
   • BTo convert the feature into a categorical variable
   • CTo rescale the feature to a range of [0, 1]
   • DTo ensure the feature has a mean of 0 and standard deviation of 1
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   Which of the following is a key characteristic of standardization (Z-score scaling)?

   • AIt uses the median and interquartile range of the feature
   • BIt uses the mean and standard deviation of the feature
   • CIt uses the minimum and maximum values of the feature
   • DIt uses the mode and range of the feature
   Reveal Solution Discussion Select an answer first
5. 19expert · hard

   A data scientist is working with a feature 'revenue' that is strictly positive, right-skewed, and contains many zero values (representing months with no sales). The scientist wants to reduce skewness while keeping the zero values meaningful for the model. Which transformation approach is most appropriate?

   • AApply log(1 + revenue) to all values.
   • BApply log(revenue) only to non-zero values and set zeros to 0.
   • CApply standardization to all values.
   • DApply square-root transformation to all values.
   Reveal Solution Discussion Select an answer first
6. 20foundation · easy

   Which type of data is the square-root transformation most commonly applied to?

   • ACount data (e.g., number of occurrences)
   • BTime series data with negative values
   • CCategorical data (e.g., color names)
   • DBinary data (e.g., 0 or 1)
   Reveal Solution Discussion Select an answer first