Which practice best supports data governance in Fabric?

• A. Rely solely on manual data discovery without catalogs.
• B. Apply only encryption; governance is irrelevant.
• C. Use policy-based controls without cataloging data owners.
• D. Use data catalogs, lineage, sensitivity labeling, and policy-driven access to govern data assets.

Answer: (D)

In data governance within Fabric, the strongest approach combines discovery, provenance, classification, and policy enforcement to create a manageable, auditable framework. A data catalog centralizes metadata, makes data assets searchable, and assigns data stewards, so users can find the right data and understand its context. Data lineage reveals where data originated and how it was transformed, which helps assess impact, trust, and regulatory compliance. Sensitivity labeling classifies data by its risk and handling requirements, guiding protection and access decisions. Policy-driven access enforces who can do what with data based on roles and the data’s classification, ensuring consistent, auditable controls across systems. When you bring these elements together, governance becomes scalable, automated, and enforceable across data assets in Fabric, enabling discoverability, responsibility, and compliance.

Relying solely on manual data discovery lacks scalability and consistency; encryption addresses protection but not governance or access control; applying governance policies without cataloging and lineage leaves data difficult to discover, understand, or trust. The combination of catalogs, lineage, sensitivity labeling, and policy-driven access provides end-to-end governance.