Customer age and purchase amount contain missing values.
Do not silently treat missingness as a meaningful value.
Choose two preprocessing practices.
Investigate why, where, and when values are missing, fit imputation on training data, and evaluate missing indicators and distribution effects.
Detailed explanation
Missingness may be random or operationally meaningful.
Missingness may be random or operationally meaningful.
Leakage and missing patterns are controlled.
Leakage and missing patterns are controlled.
Zero can be misinterpreted and create bias.
Zero can be misinterpreted and create bias.
Meaning and inference conditions diverge.
Meaning and inference conditions diverge.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2の欠損値、データ品質、前処理を確認する。Expected result
欠損を単なる空欄ではなく、発生メカニズムとモデル影響で扱える。Key points
- Missingness
- Imputation
- Leakage
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.