AI muMining Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. AI inobatsira sei mukuongorora zvicherwa?
   • Nekuchera makomba otomatiki pasina kupinza kwevanhu
   • Nekutsiva vese geologist nemarobhoti husiku
   • Nekuongorora geological uye kuchera data kufanotaura kuti ore ingangove iri kupi
   • Nekupenda mamepu nemavara akajeka
2. Why does an autonomous mining haulage system need both positioning and perception functions?
   • Positioning describes vehicle location, while perception supplies information about surroundings and obstacles
   • Positioning alone guarantees that every obstacle is detected
   • Every haul truck uses an identical camera-and-lidar configuration
   • Perception changes only the vehicle's exterior paint
3. Ndeapi makambani anozivikanwa nekushandisa marori anozvitakura munzvimbo yePilbara yeAustralia?
   • Netflix uye Spotify
   • Boeing uye Airbus
   • Coca-Cola uye PepsiCo
   • Rio Tinto uye BHP
4. What worker-exposure benefit can appropriately deployed mining automation offer?
   • Guarantee that a mine can never have an accident
   • Reduce the need for people to perform some hazardous operational tasks directly
   • Remove all operating and safety responsibilities
   • Prove that every remotely operated task uses machine learning
5. What role did BHP describe for AI at Escondida's concentrators in its 2023 announcement?
   • Use real-time plant data to recommend adjustments to operational variables affecting copper recovery
   • Guarantee that every tonne of ore contains the same amount of copper
   • Replace geological measurements with random estimates
   • Automatically establish the profitability of every future mine
6. Why are lithium and copper relevant examples in discussions of mining for energy technologies?
   • Both minerals are required to glow in the dark
   • Neither mineral has any use in electrical systems
   • Lithium is used in batteries and copper in electrical infrastructure
   • A mineral's AI prospectivity score determines its physical conductivity
7. What should a high machine-learning mineral-prospectivity score prompt?
   • Immediate certainty about ore grade and economic viability
   • Further geological investigation and evidence gathering, rather than assuming a proven recoverable deposit
   • A guarantee that no drilling or sampling is needed
   • Automatic approval of every extraction proposal
8. How does equipment condition monitoring support mining operations?
   • It proves that no component can fail after an alert is reviewed
   • It replaces geological sampling for resource estimates
   • It determines metal content from the equipment's filename
   • It uses equipment observations to flag unusual behavior and inform maintenance planning