Data Enrichment DY0-001 Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   A real estate analyst has a list of property addresses and wants to calculate the distance between each property and the city center. What is the first step to enable this distance calculation?

   • AStandardize the addresses using z-score scaling
   • BEncode the addresses using one-hot encoding
   • CGeocode the addresses to obtain latitude and longitude coordinates
   • DApply a polynomial transformation to the addresses
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2. 7application · medium

   A data scientist is building a model to predict customer lifetime value. They have a feature 'total_purchases' and 'average_purchase_value'. The scientist believes that the interaction between these two features is important. What feature engineering step should the scientist take?

   • ARemove one of the features to avoid multicollinearity
   • BApply a log transformation to each feature separately
   • CCreate a new feature that is the product of total_purchases and average_purchase_value
   • DStandardize both features and use them as is
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4. 8application · easy

   A data analyst is working with a dataset that contains a feature 'revenue' with a heavily right-skewed distribution. The analyst plans to use a linear regression model. What should the analyst do to the revenue feature to improve model performance?

   • AApply a log transformation to revenue
   • BNormalize revenue to [0, 1]
   • CStandardize revenue
   • DCreate a polynomial feature from revenue
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5. 9application · medium

   A data scientist is working on a classification problem and has a feature 'time_of_day' that is a continuous variable from 0 to 24. The scientist believes that the relationship between time of day and the target is cyclical, with similar behavior at 0 and 24. Which feature engineering approach should the scientist use?

   • ACreate a polynomial feature of degree 2 from the time feature
   • BCreate two features: sin(2π * time/24) and cos(2π * time/24)
   • CApply a log transformation to the time feature
   • DStandardize the time feature
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6. 10expert · medium

   A data scientist is building a model to predict credit risk. The dataset contains a feature 'age' with a range of 18-70 and 'credit_score' with a range of 300-850. The scientist plans to use a support vector machine (SVM) with an RBF kernel. The scientist is concerned about the scale difference. What should the scientist do?

   • ANormalize both features to [0, 1]
   • BUse the features as is because SVM is robust to scale
   • CStandardize both features before training the SVM
   • DApply a log transformation to both features
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