Recording where each dataset originated and every: What does meeting this requirement primarily involve?
Data lineage and provenance record a dataset's origin and every transformation it underwent, giving a traceable path from source to model.
The question
A governance policy requires teams to establish data lineage and provenance for every training dataset. What does meeting this requirement primarily involve?
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- Recording where each dataset originated and every transformation it underwent so its full path into the model can be traced. ✓Correct because lineage and provenance capture a dataset's origin and the transformations applied, giving a traceable history from source to model.
- Measuring the correlation between input features and the target label so the least predictive columns can be pruned from the set.Plausible because feature selection is real modeling work, but correlation analysis concerns predictive value, not the origin-and-transformation history lineage captures.
- Encrypting the dataset while it is at rest so that unauthorized parties cannot read the underlying records held in storage.Plausible because data-at-rest encryption is a valid control, but it protects confidentiality rather than documenting where data came from and how it changed.
- Estimating how many labeled examples the model needs so the team can decide whether to gather additional records before training.Plausible because sizing the dataset supports quality, but a quantity estimate is not a record of data origin and transformation history.
The trap
Confusing data lineage/provenance (origin and transformation history) with security controls or feature-engineering steps. How to remember it
Data lineage and provenance record a dataset's origin and every transformation it underwent, giving a traceable path from source to model.
How many of these would you get right?
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