Kuiz Pengesanan Anomali AI

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Mengapakah pengesanan anomali selalunya berfungsi tanpa contoh anomali yang dilabelkan?
   • Anomali jarang berlaku dan tidak dapat diramalkan, jadi model belajar 'biasa' sebaliknya
   • Label adalah haram untuk digunakan
   • Anomali tidak pernah berlaku
   • Ia hanya menggunakan imej
2. 'Anomali kontekstual' ialah titik data iaitu:
   • Selalu abnormal di mana-mana
   • Normal dalam satu konteks tetapi abnormal dalam konteks lain
   • Salah taip dalam pangkalan data
   • Rekod pendua
3. What does the cited WACV autoencoder study use as an anomaly score?
   • A legal fraud label assigned to every record
   • A guarantee that no future fault will occur
   • Reconstruction error from an autoencoder trained to model the data
   • The number of random trees in an Isolation Forest
4. How does the original Isolation Forest paper isolate candidate anomalies?
   • It requires an expert to label every anomaly in advance
   • It measures reconstruction error from a neural decoder
   • It uses a fixed threshold that proves an item is defective
   • Random partitioning tends to isolate anomalous instances in fewer splits
5. Apakah bahaya utama menetapkan ambang anomali terlalu sensitif?
   • Penganalisis dibanjiri dengan penggera palsu
   • Model berjalan lebih laju
   • Data disulitkan
   • Anomali menjadi mustahil
6. What can an unusual machine-vibration score reasonably trigger?
   • An automatic shutdown because failure is guaranteed
   • A maintenance investigation or additional measurement, without predicting failure with certainty
   • A confirmed diagnosis of the failed component
   • A claim that the machine will fail within a fixed number of hours
7. Why is an anomaly score not automatically a fraud or defect label?
   • It is always a verified label
   • Anomaly scores cannot be compared
   • It indicates unusualness under a model or baseline, not the cause or consequence
   • The threshold proves intent
8. What should be documented when setting an anomaly threshold?
   • Only the model’s name
   • A claim that false positives are impossible
   • The number of GPUs alone
   • The reference data, score definition, threshold rationale, and review process