Kediska ogaanshaha Anomaly

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay sababta moodooyinka ogaanshaha anomaly inta badan loogu tababaro xogta 'caadiga ah' halkii lagu calaamadin lahaa cillado?
   • Xogta caadiga ah waa sharci darro in la calaamadiyo
   • Moodooyinka gabi ahaanba ma farsamayn karaan cilladaha
   • Xogta caadiga ah uma baahna xisaabin
   • Cilad-xumo waa dhif iyo tusaalooyin la calaamadiyay waa dhif
2. Sidee u go'doomin kaynta u aqoonsanaysaa cillad?
   • Cilad-xumada waxay ku go'doonsan tahay qaybo yar oo random ah, taasoo siinaysa dhererka dariiqyada gaaban
   • Cilad-xumada waxay u baahan tahay kala qaybsanaan badan oo random ah si loo go'doomiyo
   • Waxay cabbirtaa midabka barta xog kasta
   • Waxa ay marka hore dhibcaha ku ururisaa calaamadahooda
3. Marka autoencoder loo isticmaalo ogaanshaha anomaly, maxaa tilmaamaya cillad?
   • Qalad dib u dhiska oo hooseeya
   • Cilad sare oo dib u dhis ah oo ku saabsan gelinta aan la aqoon
   • Waqti tabobar oo degdeg ah
   • Lakabyo badan oo shabakadda ah
4. Habka Factor Outlier Local ugu horreyntii ku tiirsan yahay?
   • Habka alifbeetada sida ay u kala horreeyaan
   • Wadarta guud ee cabbirka faylka xogta
   • Cufnaanta dhibcaha ku wareegsan bar la bixiyay
   • Liis go'an oo ah kiisaska khiyaanada la yaqaan
5. Waa kuwee caqabada dhabta ah ee la diray nidaamyada ogaanshaha cilladaha?
   • Cilad-xumada weligood isma beddelaan muddo ka dib
   • Xogta caadiga ah had iyo jeer si fiican ayaa loo calaamadeeyay
   • Outliers had iyo jeer waa xogta ugu badan
   • Qeexida 'caadiga ah' way leexan kartaa, una baahan la qabsi
6. Codkee ayaa ah isticmaalka caadiga ah ee ogaanshaha cilladaha?
   • Ogaanshaha macaamil ganacsi ee kaararka deynta
   • U kala soocida sawirada qaab alifbeeto ah
   • Turjumaada qoraalka u dhexeeya luqadaha
   • Cadaadiska feylasha muuqaalka
7. What does the contamination setting commonly represent in an Isolation Forest workflow?
   • The number of training trees that contain audio
   • An assumed or expected proportion of outliers used for thresholding
   • The probability a record was encrypted
   • The average depth of a normal point
8. Why should anomaly scores be reviewed with domain context?
   • An anomaly score always proves fraud
   • All normal data has an identical distribution
   • Rare but valid events can look unusual, and errors have different costs
   • A model score replaces an investigation