Continuously analyze factory sensor readings.
Detect behavior that differs from normal patterns early.
Which is the most appropriate AI use case?
Anomaly detection identifies observations that differ from a learned or defined normal pattern.
Detailed explanation
Finding deviations from normal time-series patterns is anomaly detection.
Safety-related controls require verification and approval.
Analysis and physical changes require separate controls.
This violates security requirements.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01ガイドのDomain 1.2にある異常検知・予測のユースケースを確認する。
Official references
What to confirm
異常検知を、通常パターンとの差を見つける分析として説明できる。
Key points
- Time-series data
- Normal patterns
- Anomaly detection
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
Baseline
A reference distribution or time pattern is needed before a deviation can be judged.
Threshold trade-off
Changing the detection threshold shifts the balance between false alarms and missed events.