Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!
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A financial analytics company is designing the compute architecture on Google Compute Engine for two distinct workloads:
Management wants to minimize infrastructure and software licensing costs while maintaining operational flexibility without locking the batch processing pipeline into rigid long-term commitments.
Which compute configuration and provisioning strategy should you recommend?
Custom machine types allow administrators to configure exact vCPU and memory allocations on supported Compute Engine machine series (such as N1, N2, N2D, and N4) rather than being constrained to rigid predefined ratios. Spot VMs are virtual machine instances provisioned from surplus Google Cloud compute capacity offered at significant discounts (60%–91%) with the caveat that Compute Engine can preempt them if capacity is reclaimed.
This architecture pairs precise resource sizing for core-licensed steady workloads with deeply discounted, pay-as-you-go preemptible resources for interruptible batch analytics, achieving maximum cost efficiency while keeping cloud infrastructure agile.
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