A churn model uses age, monthly fee, and usage count.
Handle missing values and apply the same transformations during training and inference.
Choose two appropriate practices.
Assess missing and anomalous values and use a reproducible preprocessing pipeline consistently during training and inference.
Detailed explanation
Choose imputation, exclusion, or another policy based on the data meaning and impact.
Choose imputation, exclusion, or another policy based on the data meaning and impact.
Inconsistent preprocessing changes the meaning of model inputs.
Inconsistent preprocessing changes the meaning of model inputs.
This creates training-serving skew.
This creates training-serving skew.
Zero may not represent a missing value and can introduce bias.
Zero may not represent a missing value and can introduce bias.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドDomain 1.3のデータ前処理・特徴量を確認する。Expected result
欠損処理と訓練・推論の処理一致の重要性を説明できる。Key points
- Missing values
- Reproducibility
- Training-inference consistency
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.