Implementing data lifecycle management that tracks and manages AI training data is the only option that directly addresses governance, traceability, compliance, and audit requirements throughout an AI/ML lifecycle.
AWS treats governance as a responsible AI dimension involving the incorporation of appropriate practices throughout the AI supply chain, including providers and deployers. AWS also emphasizes appropriate collection, use, management, security, and traceability of data.
A practical AWS example is Amazon SageMaker AI Lineage Tracking. AWS states that SageMaker ML Lineage Tracking “creates and stores information about the steps of a machine learning (ML) workflow from data preparation to model deployment.” It enables organizations to track model and dataset lineage and establish governance and audit standards.
For a regulated loan-approval system, an organization should be capable of determining what training data was used, where it originated, how it was processed, which training job consumed it, which model artifact was generated, and how the resulting model progressed toward deployment. AWS lineage entities can associate training datasets with training jobs and subsequent model artifacts, supporting compliance verification and reproducibility.
Option A undermines governance because changing outputs without auditable records reduces accountability and traceability.
Option C is incorrect because performance optimization cannot supersede mandatory regulatory requirements such as approved data residency, retention, and processing controls.
Option D is also incorrect. Synthetic data can sometimes reduce exposure of real personal information, but using synthetic data does not automatically satisfy AI governance requirements. Organizations still need lifecycle controls, documentation, provenance, validation, security, auditing, and regulatory compliance.
Data governance should cover acquisition, storage, processing, access, lineage, retention, deletion, and permitted uses of data throughout the ML lifecycle.
Therefore, the governance-aligned solution is B. Implement data lifecycle management to track and manage AI training data.
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