Kuis Deteksi Anomali AI

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Mengapa deteksi anomali sering kali berhasil tanpa diberi label contoh anomali?
   • Anomali jarang terjadi dan tidak dapat diprediksi, sehingga model belajar 'normal'
   • Label adalah ilegal untuk digunakan
   • Anomali tidak pernah terjadi
   • Itu hanya menggunakan gambar
2. 'Anomali kontekstual' adalah titik data yang:
   • Selalu tidak normal dimana-mana
   • Normal dalam satu konteks namun tidak normal dalam konteks lain
   • Kesalahan ketik dalam database
   • Catatan duplikat
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. Apa bahaya utama dari menetapkan ambang anomali terlalu sensitif?
   • Analis dibanjiri dengan peringatan palsu
   • Modelnya berjalan lebih cepat
   • Data dienkripsi
   • 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