1.2Standard
Purchasing behavior changes with seasons.
Separate normal cycles from persistent change.
Choose two monitoring practices.
× Incorrect
Use seasonal and weekday-aware baselines and combine distribution, performance, missingness, and label-delay signals across periods.
Detailed explanation
CorrectCombine distribution, performance, missingness, and label-delay signals across periods
Single-metric false alarms are reduced.
CorrectUse seasonal and weekday-aware reference distributions
Normal cycles are less likely to be flagged.
IncorrectRetrain after any one-day change
Noise and normal variation can trigger overreaction.
IncorrectDeclare an incident from input drift alone
Distribution change does not always imply business impact.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 1.2のデータドリフト、モデル監視、基準期間を確認する。
Official references
What to confirm
周期変動と持続的な劣化を分けてドリフトを判定できる。
Key points
- Baseline
- Seasonality
- Multiple periods
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
Reference distribution
A baseline distribution used to compare current inputs or behavior.
Drift monitoring
Watching input, output, or performance changes over time.
Question IDAWS-AIF-332
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
Report an error or rights issue