An analytics organization is designing a compute architecture on Google Cloud to process incoming data batches. The data processing pipeline has the following requirements: The processing jobs are fault-tolerant, stateless, and can gracefully recover from interruptions. Each worker instance requires precisely 6 vCPUs and 22 GB of RAM to avoid over-allocating unused memory and CPU. The compute infrastructure must scale automatically based on incoming workload volume and scale down when idle. Compute costs must be minimized to the lowest possible price. Which compute provisioning and optimization strategy should you implement?

Create a stateless Managed Instance Group (MIG) configured with Spot VMs using a custom machine type (6 vCPUs, 22 GB RAM) and enable autoscaling: ### What This Solution Is This architecture combines **Compute Engine Spot VMs**, **custom machine types**, and **stateless Managed Instance Group (MIG) autoscaling** to build a highly elastic, cost-optimized batch data processing pipeline. ### How It Meets The Requirements - **Cost minimization for fault-tolerant workloads**: **Spot VMs** provide significant discounts (up to 60–91% compared to standard on-demand pricing) by leveraging excess Compute Engine capacity, making them the most cost-effective option for interruptible batch processing. - **Exact resource allocation**: A **custom machine type** allows you to tailor the instance template to exactly 6 vCPUs and 22 GB of memory, preventing the need to step up to a larger predefined tier like `n2-standard-8` (8 vCPUs, 32 GB RAM) and eliminating waste. - **Dynamic scaling**: A **stateless MIG** supports dynamic **autoscaling policies** based on target metrics (such as CPU utilization or Pub/Sub queue depth), provisioning instances when batch workloads spike and terminating them when demand drops to reduce costs during idle periods. ### Key Advantages and Capabilities - **Maximum cost reduction**: Combines the steepest compute unit discounts via Spot pricing with rightsized resource footprints. - **Elimination of resource waste**: Prevents paying for excess vCPUs and memory overhead. - **Automated elasticity**: Automatically adjusts instance counts to match incoming demand without manual intervention. - **Resilience**: The stateless MIG automatically replaces preempted Spot instances when capacity is available. ### Why This Is Optimal This strategy directly addresses all workload constraints: it eliminates over-provisioning through custom sizing, lowers unit cost via Spot instances, and ensures resources match fluctuating demand dynamically through stateless autoscaling.

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Compute Resource Optimization Spot Custom Machine Types

Section: Data processing and compute provisioning

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An analytics organization is designing a compute architecture on Google Cloud to process incoming data batches. The data processing pipeline has the following requirements:

The processing jobs are fault-tolerant, stateless, and can gracefully recover from interruptions.
Each worker instance requires precisely 6 vCPUs and 22 GB of RAM to avoid over-allocating unused memory and CPU.
The compute infrastructure must scale automatically based on incoming workload volume and scale down when idle.
Compute costs must be minimized to the lowest possible price.

Which compute provisioning and optimization strategy should you implement?

Create a stateless Managed Instance Group (MIG) configured with Spot VMs using a custom machine type (6 vCPUs, 22 GB RAM) and enable autoscaling

Purchase calendar-mode future reservations for predefined n2-standard-8 instances with resource-based committed use discounts

Create a stateful Managed Instance Group (MIG) configured with Spot VMs using predefined e2-standard-8 machine types and enable schedule-based autoscaling

Create a stateless Managed Instance Group (MIG) configured with on-demand n2-standard-8 VMs and configure an autohealing health check policy without autoscaling

Professional Cloud Architect

Compute Resource Optimization Spot Custom Machine Types

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