Prepare and Clean Datasets SNOWPRO-ADVANCED-DATA-ANALYST Practice Questions (Page 9)

Questions 41–43

1. 41foundation · easy

   Which statistical method is commonly used to detect outliers in a dataset?

   • APivot table creation
   • BMean imputation
   • CInterquartile range (IQR) method
   • DOne-hot encoding
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2. 42application · medium

   A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The 'subscription_type' column has three categories: 'Basic', 'Premium', and 'Enterprise'. The model is a linear regression. What is the most appropriate way to encode this categorical variable?

   • AUse label encoding to assign 0, 1, and 2 to the categories.
   • BLeave the column as a string and let the model handle it.
   • CConvert the categories to their ASCII values and use them as a numeric feature.
   • DUse one-hot encoding to create three binary columns.
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3. 43application · medium

   A data scientist needs to create a training and test set from a large dataset of customer transactions for a churn prediction model. The dataset is imbalanced, with only 5% of customers being churners. The goal is to ensure the test set is representative of the overall population. What is the best approach?

   • AUse stratified sampling to ensure the test set has the same 5% churn rate as the population.
   • BUse only the churners for the test set to ensure enough examples.
   • CUse a 50/50 split to ensure a balanced test set.
   • DUse a random sample of 80% for training and 20% for testing.
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