AWS Lake Formation Cross-Account Data Sharing and Governance

The scenario requires a solution that facilitates cross-account data sharing while enforcing governance, discovery, and lineage tracking for sensitive data stored in Amazon S3. The key constraints are: the data remains in the source account (implied by 'share'), needs governance controls, must be discoverable, and lineage must be tracked.

Option D is the correct approach because AWS Lake Formation is specifically designed for building secure data lakes with centralized governance. By creating a Lake Formation data catalog, the dataset becomes discoverable across accounts via the Glue Data Catalog. Defining tag-based access controls (or column-level security) allows the data engineer to grant the ML team in another account permission to query the data directly from the source S3 bucket without copying it. Lake Formation automatically tracks data lineage, which satisfies the requirement to maintain appropriate security controls and track changes/access patterns for governance purposes.

Option A fails because direct IAM role sharing does not provide centralized governance, metadata discovery, or automatic lineage tracking; it only grants raw access. Option B is incorrect because SageMaker Unified Studio (often associated with Amazon DataZone in newer contexts) is a more complex platform for data product management, but Lake Formation is the foundational service for cataloging and securing S3-based data lakes with fine-grained access control as described. Option C involves copying data, which increases storage costs and complexity, and S3 server access logging does not provide the rich metadata, governance, or lineage features required for a governed data lake environment.