Option A is the correct solution because it uses native Amazon S3 metadata mechanisms to create a consistent, queryable, and model-friendly metadata framework with minimal complexity. S3 system metadata automatically records object creation and modification timestamps, providing reliable and consistent temporal context without additional processing.
Custom user-defined metadata is the appropriate mechanism for storing structured attributes such as author information. These key-value pairs are stored directly with the object, remain consistent across uploads, and can be accessed programmatically by downstream indexing or retrieval systems used by GenAI applications.
S3 object tags are ideal for domain classification because they are designed for lightweight categorization, filtering, and access control. Tags can be standardized across the organization to ensure consistent research domain labeling and can be consumed by search indexes or knowledge base ingestion pipelines without requiring access to the full document body.
Together, system metadata, user-defined metadata, and object tags provide a clean separation of concerns: timestamps for temporal context, metadata for authorship, and tags for classification. This structure allows foundation models to reason about document context (such as recency, domain relevance, and authorship) based on metadata alone, improving retrieval precision and reducing unnecessary token usage.
Options B, C, and D misuse features like Object Lock, access points, Storage Lens, or event notifications for purposes they were not designed for, adding complexity without improving metadata quality or model understanding.
Therefore, Option A best satisfies the metadata consistency, context enrichment, and low-overhead requirements for GenAI-driven document analysis.
