Establishing data lineage and provenance records: Which data governance practice most directly provides this
Data lineage and provenance document where each dataset originated and how it was transformed, giving end-to-end traceability.
The question
After a model produced questionable outputs, investigators need to trace exactly where each training dataset came from and how it was transformed on its way into the model. Which data governance practice most directly provides this traceability?
Preparing for AIGP? Take the free 5-min readiness quiz →
- Establishing data lineage and provenance records that document each dataset's origin and every transformation applied before training. ✓Correct: lineage and provenance capture data origins and transformations end to end, which is exactly the traceability the investigators need.
- Applying data minimization so only the attributes absolutely necessary for the model's task are collected and retained for training use.Plausible but wrong: minimization limits what data is used but does not record where data came from or how it was transformed.
- Running bias and interpretability tests on the trained model so problematic patterns in its outputs can be detected and quantified.Plausible but wrong: these tests can surface issues in outputs but do not trace the origin and transformation history of the data.
- Encrypting the datasets in transit and at rest so their integrity and confidentiality are preserved throughout the training pipeline.Plausible but wrong: encryption protects data but provides no record of source or transformation needed for traceability.
The trap
Confusing output-side testing or security controls with upstream data traceability. How to remember it
Data lineage and provenance document where each dataset originated and how it was transformed, giving end-to-end traceability.
How many of these would you get right?
One of 1581 AIGP questions on Certsqill. Take a free five-minute check and see your score per domain — not one number, but which section to open tonight.
Test your AIGP readiness — freeMore Understanding How to Govern AI Development questions
- That the organization has documented lawful rights: Following data governance requirements, what must the team →
- Bias testing that measures whether model outcomes differ: Among the planned testing activities, which type →
- Investigate the underrepresentation: Following good practice for managing issues and risks during training and →
- All 426 Understanding How to Govern AI Development questions →
Part of the Certsqill AIGP question bank · Understanding How to Govern AI Development ·
Every answer, right and wrong, comes with its own explanation.
