A banking analytics team is preparing to open-source its credit-risk model. While the model code and training pipeline are fully version-controlled in Git and Data Version Control (DVC), auditors will later need to confirm exactly which input fields were used and how each field was defined when the model was trained. According to best practices for reference data and documentation, which additional artifact should the team commit to the repository before release?
A version-controlled data dictionary file (for example, docs/data_dictionary.md) describing each feature's name, meaning, data type, and valid value range.
A note in the project README instructing users to query the production database for column details.
A static screenshot of the data-warehouse entity-relationship diagram saved in a slide deck.
The serialized binary of the trained model (model.pkl) stored in the repository's artifacts directory.
Best-practice guidance for process documentation states that each project should include human-readable reference material that clearly defines every data element used by downstream code. A data dictionary that lists each feature name together with its definition, data type, and allowed values meets this requirement because it allows anyone to interpret historical model inputs and reproduce results. Merely storing a screenshot of a schema, relying on a compiled model file, or asking users to query a live database does not provide durable, versioned definitions and therefore fails to satisfy audit and reproducibility needs.
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