Quiz buy xaaj GPU ci misaal yu bari

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Which resource boundary distinguishes separate MIG GPU Instances from software time slicing?
   • Only a different process priority on one shared execution queue
   • A guaranteed absence of every host or device failure
   • A separate network cable for every CUDA kernel
   • Dedicated hardware compute and memory resources for each GPU Instance
2. Ci ban architecture NVIDIA lañu njëkka dugal MIG?
   • Volta
   • Ampère (A100)
   • Turing
   • Paskal
3. On an A100 40 GB using supported small-instance profiles, how many GPU Instances can a layout provide?
   • Exactly sixteen on every supported GPU
   • Exactly two regardless of profile
   • Up to seven
   • An unlimited number with no memory limits
4. Ci profil MIG bu melni '1g.5gb', lan la '5gb' di tekki?
   • Memoire biñ jox instance boobu
   • limu SM yi
   • Yaatu band PCIe
   • Gaawaayu montor bi
5. Ban liggéey la MIG gëna méngoo?
   • Benn tàggat yaram bu mag te soxla GPU bi yépp
   • Inference luwekat yu bari di liggéey ak latency buñ mëna seentu
   • Done yiñ njëkka defar ci CPU
   • Dencukaay bu sedd bu yàgg
6. naka la ko Kubernetes mënee wane misaali MIG ci pod yi?
   • Ni volume disk bu ñor
   • Ci SSH manuel kese
   • Ni ay jumtukaay yuñ mëna waajal lu melni 'nvidia.com/mig-1g.5gb'
   • Jaaraleko ci dokimaa DNS
7. Two Compute Instances belong to the same MIG GPU Instance. Which resources do they share?
   • Their parent GPU Instance’s memory resources
   • Only a remote CPU’s filesystem
   • Separate complete copies of physical GPU memory
   • Every compute slice on unrelated GPUs
8. A team wants to deploy a named MIG profile on a new GPU model. What should it check first?
   • That every NVIDIA GPU supports identical profiles
   • That the model parameter count is an even integer
   • That the Kubernetes pod name contains the profile text
   • That the GPU supports MIG and that exact profile layout