Challenges in Data Preparation CT-AI Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data scientist is preparing a dataset for a neural network model. The dataset contains a feature 'height' with values in centimeters (ranging from 150 to 200) and a feature 'weight' with values in kilograms (ranging from 50 to 150). The scientist wants to ensure that both features contribute equally to the model. What is the most appropriate transformation?

   • AApply one-hot encoding to both features.
   • BApply a log transformation to the weight feature only.
   • CApply standardization (z-score normalization) to both features.
   • DRemove the height feature because it has a smaller range.
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2. 7application · medium

   A data scientist is preparing a dataset for a k-nearest neighbors (k-NN) model. The dataset contains two features: 'age' (ranging from 18 to 90) and 'annual_income' (ranging from $20,000 to $200,000). The model is currently dominated by the income feature because of its larger scale. What is the most appropriate transformation to apply?

   • AApply min-max scaling to both features so they range from 0 to 1.
   • BRemove the income feature because it is causing the model to be biased.
   • CApply one-hot encoding to both features to make them categorical.
   • DApply log transformation to the income feature to reduce its skewness.
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4. 8foundation · easy

   Which data transformation method rescales numeric features to a fixed range, typically [0, 1], by subtracting the minimum and dividing by the range?

   • AOne-hot encoding
   • BMin-max scaling
   • CImputation
   • DLabel encoding
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5. 9expert · hard

   An organization is building a model to predict customer lifetime value. The dataset includes a feature 'total_spend' which is the sum of all purchases made by the customer. The team also includes 'average_purchase_value' and 'number_of_purchases' as separate features. What is the most likely issue with this feature set?

   • AThe features are all derived from the same source, which will cause data leakage.
   • BThe features are highly correlated, which will cause the model to overfit.
   • CThe features are redundant because 'total_spend' is a linear combination of the other two, which can lead to multicollinearity and unstable model coefficients.
   • DThe features are not scaled, which will cause the model to be biased towards 'total_spend'.
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6. 10foundation · easy

   What is the primary purpose of splitting data into training, validation, and test sets?

   • ATo increase the size of the dataset
   • BTo encode categorical variables
   • CTo remove outliers from the data
   • DTo evaluate the model's performance on unseen data
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