Data scrubbing primarily aims to improve data quality by:

• A. Removing all data
• B. Rotating data values
• C. Correcting or removing inaccurate records
• D. Increasing data size

Answer: (C)

Data scrubbing, or data cleansing, focuses on making data trustworthy by finding and dealing with problems in the dataset. The core idea is to fix errors and remove or repair data that cannot be trusted so that analyses rely on accurate information. This involves correcting incorrect values, standardizing formats, resolving duplicates, and removing or flagging records that violate business rules or are otherwise invalid. By directly addressing inaccuracies, the overall quality, reliability, and usefulness of the data improve.

So, correcting or removing inaccurate records is the best fit because it directly targets the issues that degrade data quality. Other options miss the point: removing all data eliminates value, rotating data values is more about privacy or obfuscation than quality control, and simply increasing data size without fixing existing errors doesn’t improve data quality.