Professional Cloud DevOps Engineer
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Your team is architecting a compute environment for periodic AI/ML model fine-tuning and batch inference workloads. An analysis of the workload profile shows the following characteristics:
Which compute provisioning model and service configuration should you select to optimize costs while satisfying workload constraints?
Use the Flex-start provisioning model with GKE or Compute Engine Managed Instance Groups (MIGs) using resize requests.
Use Standard on-demand instances with custom machine types and 1-year committed use discounts (CUDs).
Use Spot VMs configured within a stateless Compute Engine Managed Instance Group with autoscaling.
Use Future Reservations in calendar mode to lock in dedicated compute capacity for 90 days.
Use the Flex-start provisioning model with GKE or Compute Engine Managed Instance Groups (MIGs) using resize requests.
Flex-start is a Google Cloud compute consumption and provisioning model designed for short-duration workloads (lasting up to seven days) that require compute resources and accelerators like GPUs. Under this model, Compute Engine schedules and provisions the requested compute instances as soon as capacity becomes available in the selected zone, providing access without requiring manual reservation negotiations.
Flex-start directly matches the combination of flexible start times, multi-day non-preemptible execution, and pay-as-you-go cost reduction, outperforming Spot VMs (which risk preemption) and fixed calendar reservations (which charge for idle reservation capacity).
Use Standard on-demand instances with custom machine types and 1-year committed use discounts (CUDs).
Use Spot VMs configured within a stateless Compute Engine Managed Instance Group with autoscaling.
Use Future Reservations in calendar mode to lock in dedicated compute capacity for 90 days.