Objective 2.4 Transform Numerical and Categorical Data AIP-210 Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is the primary benefit of designing a data transformation pipeline as a series of sequential steps rather than applying transformations ad hoc?

   • AIt reduces the number of features in the dataset
   • BIt eliminates the need for feature scaling
   • CIt ensures that the same transformations are applied consistently to training and test data
   • DIt automatically selects the best machine learning model
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A data analyst is preparing data for a principal component analysis (PCA). The 'TransactionAmount' feature has a few extreme values that are legitimate (e.g., large corporate purchases). The analyst wants to reduce the influence of these outliers on the PCA results while preserving the data points. Which approach should the analyst use?

   • AApply min-max scaling to the feature.
   • BApply robust scaling using the median and interquartile range.
   • CApply standardization (z-score).
   • DRemove the outlier rows.
   Reveal Solution Discussion Select an answer first
4. 13foundation · easy

   Which feature scaling technique is most appropriate when the data contains outliers, because it uses the median and interquartile range instead of the mean and standard deviation?

   • AMin-max scaling
   • BRobust scaling
   • CStandardization (z-score)
   • DLog transformation
   Reveal Solution Discussion Select an answer first
5. 14foundation · easy

   A data scientist wants to reduce the impact of outliers in a numerical feature without removing data points. Which technique is most appropriate?

   • AOne-hot encoding
   • BCapping (winsorization)
   • CStandardization
   • DRemoving outliers
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   What is the primary purpose of binning (discretization) a continuous numerical feature?

   • ATo handle missing values in the feature
   • BTo scale the feature to a fixed range
   • CTo convert the continuous feature into categorical bins or intervals
   • DTo reduce the number of features in the dataset
   Reveal Solution Discussion Select an answer first