Your organization wants to classify data based on its sensitivity and regulatory requirements. Which of the following methods would be most appropriate for implementing data classification within…

Azure Information Protection (AIP) labels, now often integrated with Microsoft Purview Information Protection, are specifically designed for classifying data based on its sensitivity and regulatory requirements. These labels can be applied at various levels in Fabric, providing a consistent and enforceable mechanism for data governance and protection.

Custom tags in Azure Data Factory are primarily for organizing and filtering pipelines or datasets within Data Factory itself, not for enterprise-wide data sensitivity classification or regulatory compliance.

Row-level security (RLS) restricts who can see which rows of data based on permissions, which is an access control mechanism, not a method for classifying the inherent sensitivity of the data itself.

Data masking is a technique to obfuscate or hide sensitive data from unauthorized users, serving as a data protection measure, but it is not a method for classifying the data's sensitivity or regulatory requirements.