How would you implement time-based retention in a lakehouse to comply with policy?

• A. Manual purge data on a quarterly basis
• B. Disable retention to preserve data indefinitely
• C. Retention is not supported in Fabric lakehouse
• D. Use retention policies by dataset with automatic purging or archiving after defined retention periods; ensure backups or point-in-time restore.

Answer: (D)

Time-based retention in a lakehouse is implemented with dataset-level retention policies that automatically purge or archive data after defined retention periods. This approach enforces the data lifecycle consistently, reduces manual tasks, and helps meet policy requirements. You can configure each dataset with its own retention window, and after that window the system automatically purges or moves data to an archive. To protect against accidental deletion or to support audits, maintain backups or enable point-in-time restore so you can recover data if needed. Manual purges are error-prone and inconsistent, disabling retention misses policy requirements, and saying retention isn’t supported is incorrect in a typical lakehouse setup.