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: Execution Duration: Jobs run continuously for 4 to 6 days per execution cycle. Fault Tolerance: Workloads cannot tolerate mid-execution interruptions or preemptions due to high state-synchronization overhead and strict delivery timelines. Scheduling Flexibility: Jobs are queue-based and can wait to start until capacity becomes available within a flexible scheduling window. Cost Target: The organization requires significant cost savings over on-demand pricing without purchasing long-term continuous reservations. 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.: ### What Flex-start is **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. ### How it meets the requirements - **Execution Duration:** Flex-start instances support run durations of up to **seven days**, perfectly accommodating jobs that require 4 to 6 days of continuous runtime. - **Fault Tolerance:** Unlike Spot instances, Flex-start VMs are **non-preemptible** during their active run duration, guaranteeing that multi-day fine-tuning or inference tasks run without unexpected termination. - **Scheduling Flexibility:** Flex-start operates on a best-effort, delayed-start scheduling queue, which fits workloads that can start whenever capacity is available. - **Cost Optimization:** The provisioning model provides substantial **discounts of up to 53%** for vCPUs, memory, and GPUs compared to standard on-demand pricing on a pay-as-you-go (PAYG) basis, without requiring long-term upfront commitments. ### Key advantages and capabilities - **Pay-as-you-go discounting:** Delivers up to a 53% discount while billing only for the actual run duration of the provisioned instances. - **Orchestration integration:** Flex-start natively integrates with **Google Kubernetes Engine (GKE)** and **Compute Engine Managed Instance Groups (MIGs)** using resize requests. - **Densely allocated hardware:** Ensures optimal interconnectivity and low network latency for compute-intensive accelerator configurations. - **Zero over-provisioning waste:** Automatically terminates instances at the conclusion of the specified run duration. ### Why this is optimal 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).

Workload Service Selection Flex Start Compute Engine | Professional Cloud DevOps Engineer

Workload Service Selection Flex Start Compute Engine

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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:

Execution Duration: Jobs run continuously for 4 to 6 days per execution cycle.
Fault Tolerance: Workloads cannot tolerate mid-execution interruptions or preemptions due to high state-synchronization overhead and strict delivery timelines.
Scheduling Flexibility: Jobs are queue-based and can wait to start until capacity becomes available within a flexible scheduling window.
Cost Target: The organization requires significant cost savings over on-demand pricing without purchasing long-term continuous reservations.

Which compute provisioning model and service configuration should you select to optimize costs while satisfying workload constraints?

Use Standard on-demand instances with custom machine types and 1-year committed use discounts (CUDs).

Use the Flex-start provisioning model with GKE or Compute Engine Managed Instance Groups (MIGs) using resize requests.

Use Future Reservations in calendar mode to lock in dedicated compute capacity for 90 days.

Use Spot VMs configured within a stateless Compute Engine Managed Instance Group with autoscaling.

Professional Cloud DevOps Engineer

Workload Service Selection Flex Start Compute Engine

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