A data analyst is working with an EmployeeTimeLog table containing HoursWorked and HourlyRate columns for each employee's weekly activity. According to company policy, any hours worked over 40 in a week are paid at 1.5 times the normal hourly rate. The analyst needs to add a GrossPay column to their dataset for a payroll report. Which data transformation technique is most appropriate for creating this new column?
Perform imputation to fill in any missing pay values.
Create a derived variable for GrossPay using a conditional expression.
Apply binning to the HoursWorked column to categorize employees.
Use an aggregate function to summarize the HoursWorked and HourlyRate columns.
The correct answer is to create a derived variable, also known as a calculated field. This technique involves creating a new column by applying a formula or a set of rules to existing columns. In this scenario, a conditional statement (like a CASE statement in SQL) would be used to check if HoursWorked is greater than 40 and then apply the appropriate calculation for regular pay and overtime pay to derive the GrossPay for each row.
Binning is incorrect because it is used to group a range of continuous values into a smaller number of 'bins' or categories. It would be useful for grouping employees by hours worked (e.g., '0-20', '21-40', '41+'), but not for calculating their specific pay.
Using an aggregate function is incorrect. Aggregate functions like SUM() or AVG() operate on multiple rows to return a single summary value. This task requires a new value to be calculated for each individual employee row, not a summary of the entire dataset.
Imputation is incorrect because it is a technique used to fill in missing values in a dataset. The scenario does not state that there is any missing data to be handled; rather, it requires the creation of new data from existing values.
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CompTIA Data+ DA0-002 (V2)
Data Acquisition and Preparation
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