Data Enrichment DY0-001 Practice Questions (Page 3)

Questions 11–15

1. 11application · easy

   A data scientist is building a linear regression model and suspects that the relationship between a predictor X and the target Y is quadratic. The current model has poor performance. What should the scientist do to capture this relationship?

   • AApply a log transformation to X
   • BRemove X from the model
   • CAdd a polynomial feature X^2 to the model
   • DStandardize X
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A data scientist is building a churn prediction model and has a dataset with a 'customer tenure' column in days. The model will also include a 'monthly charges' column. The scientist suspects that the relationship between tenure and churn is not linear and that the scale of the two features differs greatly. Which two-step approach should the scientist take to prepare the features?

   • AApply a log transformation to tenure, then standardize both tenure and monthly charges.
   • BNormalize both features, then create a polynomial feature from the normalized tenure.
   • CApply a polynomial transformation to monthly charges, then normalize tenure only.
   • DStandardize both features first, then apply a log transformation to the standardized tenure.
   Reveal Solution Discussion Select an answer first
4. 13application · medium

   A data scientist is training a neural network with features that have different scales. The network is converging slowly. The scientist suspects that the scale differences are causing the optimizer to struggle. What should the scientist do to improve convergence?

   • ARemove features with large scales
   • BApply a log transformation to all input features
   • CIncrease the learning rate
   • DStandardize all input features
   Reveal Solution Discussion Select an answer first
5. 14expert · medium

   A data scientist is working with a dataset where the relationship between a predictor X and the target Y is cubic. The scientist is using a linear regression model. What should the scientist do to capture this relationship?

   • AAdd polynomial features X^2 and X^3 to the model
   • BApply a log transformation to X
   • CStandardize X
   • DRemove X from the model
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   A data scientist is preparing a dataset for a machine learning model. The dataset contains a 'date' column and a 'price' column. To capture the recurring seasonal pattern in the data, which new feature would be most appropriate to engineer?

   • AA 'day_of_week' feature derived from the date
   • BA 'date_string' feature that converts the date to a string format
   • CA 'price_minus_mean' feature representing deviation from the average price
   • DA 'price_squared' feature calculated as price^2
   Reveal Solution Discussion Select an answer first