1.1Basic
Use known fraud labels for a classifier.
Find groups in unlabeled customer behavior.
Choose two mappings.
× Incorrect
Supervised learning uses labeled examples for classification or regression; unsupervised learning explores structure without target labels.
Detailed explanation
CorrectUse supervised learning for predictions from known labels
Inputs are paired with target outcomes.
Inputs are paired with target outcomes.
CorrectUse unsupervised learning to explore unlabeled similarity groups
Structure can be found without targets.
Structure can be found without targets.
IncorrectUnsupervised learning always requires target labels
It can use unlabeled data.
It can use unlabeled data.
IncorrectSupervised learning has no target variable
Labels define what is learned.
Labels define what is learned.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.1の教師あり・教師なし学習を確認する。Expected result
ラベルの有無と目的に応じて学習方式を選べる。Key points
- Supervised
- Unsupervised
- Labels
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.
Question IDAWS-AIF-313
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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