Which of the following is not an example of data scrubbing?

• A. Handling marginal data
• B. Correcting data entry errors
• C. Removing duplicate records
• D. Standardizing formats

Answer: (A)

The idea being tested is what actions qualify as data scrubbing (data cleansing) versus other data quality activities. Data scrubbing focuses on making data accurate and consistent for analysis by directly altering the data to fix issues. Correcting data entry errors is a classic scrubbing step because it fixes mistakes that would mislead analyses. Removing duplicate records is another key scrubbing activity because duplicates distort results and inflating counts. Standardizing formats is also a standard cleaning operation, ensuring consistency across fields so comparisons and joins work reliably.

Handling marginal data isn’t typically classified as data scrubbing. It’s more about deciding what to do with data that’s on the edge of usability—whether to flag it, exclude it, or apply higher-level governance rules—rather than a direct cleaning action that fixes specific data issues. So the option describing handling marginal data isn’t an example of data scrubbing, while the other three are.