Explain which transformations produced a model.
Reprocess data when a source is wrong.
Choose two practices.
Link source, extraction, transformation, labeling, splitting, features, model, and deployment with versions, owners, schemas, approvals, and quality results.
Detailed explanation
Impact and reproducibility are visible.
Impact and reproducibility are visible.
Changed assets and reprocessing scope are identifiable.
Changed assets and reprocessing scope are identifiable.
Root cause and impact cannot be traced.
Root cause and impact cannot be traced.
Affected models are unknown.
Affected models are unknown.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3のデータ系譜、MLOps、モデル再現性を確認する。Expected result
モデルへ至るデータ変換と責任・版を追跡し、問題データの影響を特定できる。Key points
- Lineage
- Impact
- Reprocessing
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.