Objective 3.2 Preprocess Data CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Which of the following is a common method to detect missing values in a dataset?

   • AUsing df.fillna() to replace missing values
   • BUsing df.dropna() to remove rows with missing values
   • CUsing df.astype() to change data types
   • DUsing df.describe() to view summary statistics
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2. 7application · medium

   A data scientist is cleaning a dataset with 10,000 rows. The column 'customer_id' has 2% missing values, and the column 'purchase_amount' has 50% missing values. The data scientist needs to prepare the data for a clustering algorithm. The business requirement is to keep as many records as possible while avoiding bias. What is the most appropriate action?

   • ADelete rows with missing 'customer_id' and impute 'purchase_amount' with the mean.
   • BImpute 'customer_id' with the mode and 'purchase_amount' with the median.
   • CDelete all rows with missing values in either column.
   • DDelete the 'customer_id' column and impute 'purchase_amount' using model-based imputation.
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4. 8expert · hard

   A data scientist is analyzing a dataset with 10,000 rows. The column 'response_time' has missing values. A missingness map shows that missing values are concentrated in rows where 'service_type' is 'premium'. The 'service_type' column is complete. The data scientist needs to decide how to handle the missing values. The business requirement is to minimize bias in a regression model predicting response_time. What is the most appropriate approach?

   • ADelete rows with missing 'response_time'.
   • BImpute 'response_time' separately for each service_type using the median.
   • CImpute 'response_time' with the mode.
   • DImpute 'response_time' with the overall mean.
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5. 9application · medium

   A data analyst is cleaning a dataset with 50,000 rows. The column 'employee_id' has 5% missing values, and the column 'salary' has 40% missing values. The analyst needs to prepare the data for a regression model. The business requirement is to minimize bias in the model. What is the most appropriate action?

   • AImpute 'employee_id' with the mode and 'salary' with the median.
   • BDelete the 'employee_id' column and impute 'salary' using a model-based approach.
   • CDelete rows with missing 'employee_id' and impute 'salary' with the mean.
   • DDelete rows with missing 'salary' and keep 'employee_id' as is.
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6. 10application · medium

   A data scientist is building a linear regression model with features 'height' (in cm) and 'weight' (in kg). The model uses regularization (Ridge). The data scientist wants to ensure that the regularization penalty treats both features fairly regardless of their units. What should the data scientist do?

   • AApply robust scaling to both features.
   • BApply standardization (z-score) to both features.
   • CDo not scale the features because linear regression is scale-invariant.
   • DApply min-max scaling to both features.
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