What constitutes a data audit in a Clarity model, and which fields typically support it?

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Multiple Choice

What constitutes a data audit in a Clarity model, and which fields typically support it?

Explanation:
At the heart of a data audit is keeping a history of changes to a record: who made the change, when it happened, and why. This enables accountability, traceability, and the ability to understand how data evolved over time. In a Clarity model, the fields that support this are created_by and created_at, which capture who created the record and when. updated_by and updated_at log who last changed the record and when that change occurred. The change_reason field adds the justification for the modification, providing important context beyond the timestamps and user IDs. This combination gives a complete audit trail: you can see the origin, track every update, and understand the rationale behind edits. It’s not just about performance, data types, or deletions; those fields specifically enable a transparent record of data changes and their context.

At the heart of a data audit is keeping a history of changes to a record: who made the change, when it happened, and why. This enables accountability, traceability, and the ability to understand how data evolved over time.

In a Clarity model, the fields that support this are created_by and created_at, which capture who created the record and when. updated_by and updated_at log who last changed the record and when that change occurred. The change_reason field adds the justification for the modification, providing important context beyond the timestamps and user IDs.

This combination gives a complete audit trail: you can see the origin, track every update, and understand the rationale behind edits. It’s not just about performance, data types, or deletions; those fields specifically enable a transparent record of data changes and their context.

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