Data Cleaning and Standardization PCAD-31 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the result of applying z-score standardization to a feature?

   • AValues are converted to integers
   • BValues are rescaled to a range of 0 to 1
   • CValues have a mean of 0 and a standard deviation of 1
   • DValues are sorted in ascending order
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2. 17foundation · easy

   What is the primary purpose of data normalization?

   • ATo reduce the number of features in the dataset
   • BTo bring different features to a common scale without distorting differences in ranges
   • CTo convert categorical variables into numerical ones
   • DTo remove duplicate records from the dataset
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4. 18expert · hard · select all that apply

   A data analyst is cleaning a dataset of customer transactions. The 'transaction_amount' column contains a mix of positive and negative values. Negative values are valid (refunds), but some negative values are extremely large (e.g., -$100,000) compared to the typical refund of -$50. The 'customer_id' column has some missing values. The analyst needs to prepare the data for a regression model. Select all that apply: which steps are appropriate?

   • ARemove all rows with negative transaction amounts.
   • BImpute missing 'customer_id' values with a placeholder like 'UNKNOWN'.
   • CApply z-score standardization to the 'transaction_amount' column.
   • DInvestigate the extremely large negative values to determine if they are valid refunds or data entry errors.
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5. 19foundation · easy

   Which of the following is an example of structured data?

   • AA set of social media posts
   • BA table of sales transactions with columns for date, product, and amount
   • CA video recording of a product demo
   • DA collection of customer emails
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6. 20application · medium

   A data analyst is cleaning a dataset of product prices. The 'price' column contains values like '$12.99', '12.99 USD', and '12,99'. The analyst needs to compute the total revenue. What is the most appropriate cleaning step?

   • ARemove all rows where the price is not in the format '$12.99'.
   • BKeep the price column as a string and concatenate all values.
   • CStandardize the price column by removing currency symbols, converting commas to decimal points, and casting to a numeric type.
   • DApply min-max scaling to the price column to normalize it.
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