Data lineage in AI asset inventory means:

• A. Counting data points.
• B. Measuring data compression.
• C. Tracing the origin and transformations of data through its lifecycle.
• D. Listing data sources only.

Answer: (C)

Data lineage is about tracing the origin and transformations of data through its lifecycle. In an AI asset inventory this means documenting where the data came from, every step it underwent (ingestion, cleaning, feature engineering, joins, etc.), and where it is used (such as model training or evaluation). This provenance lets you understand dependencies, reproduce experiments, assess risk when a data source changes, and verify compliance with governance and privacy policies. It also helps surface data quality issues and potential biases by following the data’s journey from source to model input.

The other options don’t capture this full picture. Counting data points only measures quantity, not where the data came from or how it was transformed. Measuring data compression focuses on data size, not data provenance. Listing data sources alone provides origins but omits the transformations and processing steps that create the final data used in AI workflows.