What data retention strategies are typical in Fabric?

• A. Define retention policies per dataset, archive or purge stale data, support point-in-time restore, and implement lifecycle management to balance cost and compliance.
• B. Retain all data forever with no policy.
• C. Purge everything after a fixed period regardless of usage.
• D. Retention is not configurable in Fabric.

Answer: (A)

In Fabric, data retention is handled through policy-driven controls that apply to each dataset, balancing cost, compliance, and recoverability. Defining retention policies per dataset lets different datasets follow the specific regulatory or business needs they have. Archiving older data moves it to cheaper storage while keeping it accessible if needed, and purging stale data helps control costs and meet privacy rules. Point-in-time restore provides a way to recover data from a specific moment, which is important after mistakes or ransomware within the retention window. Lifecycle management automates moving and deleting data across storage tiers to maintain ongoing cost and compliance balance. The other options don’t fit because keeping everything forever has no policy, purging everything after a fixed period overlooks value and usage, and retention is indeed configurable in Fabric.