An analyst joins several sources and inspects missing values and outliers.
Save the preparation steps as a flow that can be reviewed and reused.
Which approach is most appropriate?
Use SageMaker Data Wrangler to connect, transform, profile, and save a reproducible data flow.
Detailed explanation
Lifecycle rules do not transform data values or evaluate quality.
Lifecycle rules do not transform data values or evaluate quality.
Manual edits lack scalable execution, review, and reproducibility.
Manual edits lack scalable execution, review, and reproducibility.
Connections, transformations, visualization, and quality checks are represented as reusable steps.
Connections, transformations, visualization, and quality checks are represented as reusable steps.
API history is not a preparation flow for the business dataset.
API history is not a preparation flow for the business dataset.
Check official documentation
Compare the following points with the official documentation.
Amazon SageMaker Data Wrangler data flow、transform、data qualityの公式説明を確認する。
What to confirm
手作業変換と再利用可能なデータフローの違いを説明できる。
Key points
- Data Wrangler
- Data flow
- Preprocessing
- Reproducibility
Notes
- Environment: AWS公式DEA-C01試験ガイドと各サービスの公式ドキュメントの確認
- Confirm current AWS service behavior in the official DEA-C01 documentation.
Foundation review
Exploration versus production
Promote tested transformations into governed jobs rather than relying on ad-hoc edits.
Lineage
Record source versions, parameters, and outputs for each transformation step.