Monitor server metrics.
Detect a sharp change that differs from normal behavior.
Which AI use case fits?
Anomaly detection identifies observations that deviate from a normal baseline or pattern.
Detailed explanation
It detects observations that depart from normal patterns.
Translation converts text between languages.
Encryption protects confidentiality rather than detecting metric deviations.
Generation creates images from inputs or conditions.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01ガイドDomain 1.2で異常検知のユースケースを確認する。
Official references
What to confirm
正常パターンからの逸脱を異常検知として説明できる。
Key points
- Normal baseline
- Deviation
- Monitoring
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
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Normality definition
An anomaly cannot be judged without a reference pattern, time window, or domain rule.
Alert trade-off
A sensitive threshold can find more anomalies while also producing more false alarms.