You just hired an outside developer to modernize some applications with new web services and functionality.
In order to implement a comprehensive test platform for validation, the developer needs a data set that resembles a production data set in both size and composition.
In order to accomplish this, what type of masking would you use?
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A. B. C. D.C.
Static masking takes a data set and produces a copy of it, but with sensitive data fields masked.
This allows for a full data set from production for testing purposes, but without any sensitive data.
Dynamic masking works with a live system and is not used to produce a distinct copy.
The terms "replicated" and "development" are not types of masking.
The term "data masking" refers to the process of creating a structurally similar but fictitious version of an organization's data for use in situations where the original data is not required. This technique is often used for data security reasons, such as protecting sensitive data or complying with data protection laws.
In this scenario, the outside developer needs a data set that resembles a production data set in both size and composition to implement a comprehensive test platform for validation. To accomplish this, the appropriate type of data masking would be "replicated" data masking.
Replicated data masking creates a copy of the original data set, with all the same fields and relationships, but replaces sensitive or confidential data with fictitious data. The goal is to retain the original structure and format of the data, while ensuring that it cannot be traced back to the original data set.
The replicated data masking approach is useful when the data set is complex and requires specific relationships between the fields. By maintaining the original data structure, the test platform can accurately replicate real-world scenarios and identify any issues that may arise during testing.
The other options presented in the answer choices are less suitable for this scenario.
Development data masking creates a fictitious data set that resembles the original data set but with different values. This approach is useful for development environments but not for creating a data set that resembles a production data set.
Static data masking is a one-time data masking process that creates a fixed set of fictitious data that is used for all testing. This approach is suitable when the data set is simple and does not require specific relationships between fields.
Dynamic data masking is a technique that is used to limit sensitive data in real-time, typically by masking or redacting sensitive data fields when they are accessed. This approach is not suitable for creating a test platform.
In summary, the appropriate type of data masking to use in this scenario is replicated data masking, which creates a copy of the original data set with all the same fields and relationships, but replaces sensitive or confidential data with fictitious data.