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 2)

Questions 6–10

1. 6application · medium

   A data analyst is working with a feature 'particle_count' that represents counts of particles in a sample. The distribution is right-skewed and the variance appears to increase with the mean. The analyst wants to stabilize the variance and reduce the skewness while keeping the transformation simple and avoiding issues with zero values. Which transformation should be applied?

   • AStandardization (z-score)
   • BMin-max normalization
   • CLogit transformation
   • DSquare-root transformation
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   Which transformation is most appropriate when you need to rescale a feature to a specific bounded range, such as [0, 1]?

   • ASquare-Root Transformation
   • BStandardization (Z-score)
   • CLog Transformation
   • DNormalization (Min-Max Scaling)
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A data scientist is building a logistic regression model to predict the probability of customer churn. One feature, 'loyalty_score', is a proportion between 0 and 1 representing the fraction of purchases made in the last year. The scientist wants to use this feature directly in the linear model but is concerned that the bounded nature of the proportion may violate the linearity assumption. Which transformation should be applied to 'loyalty_score'?

   • ALogit transformation
   • BMin-max normalization
   • CStandardization (z-score)
   • DLog transformation
   Reveal Solution Discussion Select an answer first
5. 9foundation · easy

   Which of the following is a limitation of the log transformation?

   • AIt is only applicable to categorical features
   • BIt increases the variance of the feature
   • CIt can only be applied to positive values
   • DIt always produces a normal distribution
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6. 10foundation · easy

   What is the primary use case for applying a log transformation to a feature?

   • ATo handle positively skewed data and compress the scale of large values
   • BTo rescale the feature to a range of [0, 1]
   • CTo convert categorical data into numerical data
   • DTo ensure the feature has a mean of 0 and standard deviation of 1
   Reveal Solution Discussion Select an answer first