Kediska ogaanshaha AI Anomaly

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Maxay tahay sababta ogaanshaha anomaly inta badan u shaqeyso iyada oo aan la calaamadin tusaalayaal cillado?
   • Cilad-xumada waa naadir lamana saadaalin karo, marka moodooyinka waxay bartaa 'caadi'
   • Calaamaduhu waa sharci darro in la isticmaalo
   • Cilad-xumo weligeed ma dhacdo
   • Waxay isticmaashaa sawirro kaliya
2. A 'contextual anomaly' waa barta xogta oo ah:
   • Had iyo jeer aan caadi ahayn meel kasta
   • Caadi marka la eego macnaha guud laakiin mid kale ayaan caadi ahayn
   • Qoraal ku jira kaydka xogta
   • Rikoor nuqul ah
3. Maxay daraasadda autoencoder-ka WACV ee la soo xigtay u isticmaashaa dhibco aan caadi ahayn?
   • 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. Sidee bay warqaddii asalka ahayd ee Go'doonka kaynta u go'doomin lahayd cilladaha murashaxiinta?
   • 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. Waa maxay khatarta ugu weyn ee dejinta marinka cilladda si aad xasaasi u ah?
   • Falanqeeyayaasha ayaa ku soo qulqulaya digniino been abuur ah
   • Qaabku si dhakhso leh ayuu u socdaa
   • Xogtu waa la sirdoonaa
   • Cilad-xumada waxay noqotaa mid aan macquul ahayn
6. Maxay tahay natiijada gariirka mashiinka aan caadiga ahayn ee si macquul ah u kicin kara?
   • 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