Professional Cloud DevOps Engineer
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An enterprise runs three distinct workloads on Google Cloud Compute Engine:
Which combination of instance configurations and purchasing models should the DevOps engineer implement to minimize total infrastructure costs while maintaining reliability?
Provision custom machine types (24 vCPUs, 48 GB RAM) covered by resource-based Committed Use Discounts (CUDs) for the backend, deploy a stateless Managed Instance Group (MIG) with autoscaling for the web frontend, and use Spot VMs for the batch processing pipeline.
Deploy the backend service and the batch processing pipeline on Spot VMs within a regional Managed Instance Group with autohealing, and purchase resource-based CUDs for peak autoscaled capacity on the web frontend.
Standardize all instances on predefined general-purpose n2-standard-32 machine types, apply automatic sustained use discounts (SUDs), and disable horizontal autoscaling to avoid operational overhead.
Provision standard predefined memory-optimized machine types (m1 series) covered by 3-year spend-based CUDs across all tiers, including the batch image processing pipeline.
Provision custom machine types (24 vCPUs, 48 GB RAM) covered by resource-based Committed Use Discounts (CUDs) for the backend, deploy a stateless Managed Instance Group (MIG) with autoscaling for the web frontend, and use Spot VMs for the batch processing pipeline.
This strategy combines custom machine types, resource-based Committed Use Discounts (CUDs), stateless Managed Instance Groups (MIGs) with autoscaling, and Spot VMs to align infrastructure purchase models precisely with workload profiles and utilization metrics.
StudyCerts
StudyCertsn1-standard or n2-standard) enforce standard resource ratios of 1 vCPU to 4 GB RAM. For a workload requiring 24 vCPUs and 48 GB RAM (1:2 ratio), predefined sizing would force provisioning a standard 24 vCPU / 96 GB RAM instance or an oversized machine, creating 48 GB of unutilized, paid RAM. Provisioning a custom machine type aligns allocation directly to the 1:2 ratio. Backing this steady-state baseline with a 1-year or 3-year resource-based Committed Use Discount cuts baseline compute costs by up to 55% without incurring waste.This architecture adheres to core FinOps principles by pairing predictable, non-standard footprints with custom sizing and commitments, while utilizing elasticity and deeply discounted preemptible capacity where architectural fault tolerance permits.
Deploy the backend service and the batch processing pipeline on Spot VMs within a regional Managed Instance Group with autohealing, and purchase resource-based CUDs for peak autoscaled capacity on the web frontend.
Standardize all instances on predefined general-purpose n2-standard-32 machine types, apply automatic sustained use discounts (SUDs), and disable horizontal autoscaling to avoid operational overhead.
Provision standard predefined memory-optimized machine types (m1 series) covered by 3-year spend-based CUDs across all tiers, including the batch image processing pipeline.