Multi-Instance GPU Partitioning Quiz

Answers and explanations appear as you take the quiz.

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. On which NVIDIA architecture was MIG first introduced?
   • Volta
   • Ampere (A100)
   • Turing
   • Pascal
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. In a MIG profile like '1g.5gb', what does the '5gb' represent?
   • The memory allocated to that instance
   • The number of SMs
   • The PCIe bandwidth
   • The clock speed
5. Which workload is MIG especially well suited for?
   • A single massive training run needing the whole GPU
   • Multi-tenant inference serving with predictable latency
   • CPU-bound data preprocessing
   • Long-term cold storage
6. How can a Kubernetes cluster expose MIG instances to pods?
   • As raw disk volumes
   • Only through manual SSH
   • As schedulable resources like 'nvidia.com/mig-1g.5gb'
   • Via DNS records
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