What method creates a structurally similar but inauthentic version of data for testing and training purposes?

The method that creates a structurally similar but inauthentic version of data for testing and training purposes is data masking. This technique involves altering original data in such a way that it remains usable for analysis and testing while ensuring that sensitive information cannot be accessed or deduced.

Data masking is particularly important in environments where personal or confidential information must be protected, such as in healthcare or finance. By replacing original data with modified values that maintain the same format and structure, developers and testers can work with realistic datasets without exposing sensitive information. This allows organizations to comply with privacy regulations while still meeting the needs for development and testing.

The other options, while related to data handling, serve different purposes. Data duplication involves creating exact copies of data, which does not provide the inauthenticity needed for secure testing. Data simulation typically refers to generating data that mimics real-world scenarios but may not necessarily relate to the original dataset’s structure. Data replication focuses on copying data from one location to another, often for backup or redundancy purposes, rather than altering it for security.