Data Wrangling DY0-001 Practice Questions (Page 2)

Questions 6–10

1. 6expert · hard

   A data scientist is building a predictive model for a clinical trial. The dataset has a 'blood_pressure' column with missing values that are more likely for patients with a certain condition. The scientist wants to impute the missing values in a way that preserves the relationship between blood pressure and the condition. Which imputation method is most appropriate?

   • ARegression imputation using the condition as a predictor
   • BMean imputation
   • CMode imputation
   • DListwise deletion of rows with missing blood pressure
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A data analyst at a retail company is preparing a customer purchase history dataset for a churn prediction model. The dataset contains a column 'purchase_amount' with some values recorded as negative numbers due to refunds, and a column 'customer_age' with several missing entries. The model will be trained on the entire dataset, and the analyst wants to preserve as much information as possible while ensuring data quality. What should the analyst do?

   • ARemove all rows with negative purchase amounts and impute missing customer ages with the median age.
   • BKeep negative purchase amounts as valid refund indicators and impute missing customer ages with the median age.
   • CReplace negative purchase amounts with zero and delete rows with missing customer ages.
   • DConvert negative purchase amounts to their absolute values and impute missing customer ages with the mean age.
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   Which merging method is used to combine two datasets that have the same columns by stacking them on top of each other?

   • ALookup
   • BOuter join
   • CConcatenation
   • DInner join
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A data analyst is combining two datasets: 'sales' (date, store_id, revenue) and 'store_info' (store_id, region, manager). The 'sales' dataset has records for all stores, but 'store_info' is missing a few stores. The analyst wants to include all sales records and fill in missing region information with 'Unknown'. What should the analyst do?

   • APerform an inner join on store_id, then replace null region values with 'Unknown'.
   • BPerform a full outer join on store_id, then replace null region values with 'Unknown'.
   • CPerform a right join on store_id, then replace null region values with 'Unknown'.
   • DPerform a left join on store_id, then replace null region values with 'Unknown'.
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   A dataset has a column with missing values for a numeric feature. Which imputation strategy replaces missing values with the average of the observed values in that column?

   • AMedian imputation
   • BMean imputation
   • CK-Nearest Neighbors (KNN) imputation
   • DMode imputation
   Reveal Solution Discussion Select an answer first