A cloud engineer is designing Google Cloud compute infrastructure for a healthcare organization with two distinct application requirements:
1. Workload 1: An asynchronous, fault-tolerant batch image processing pipeline that can resume cleanly if an instance is terminated unexpectedly.
2. Workload 2: A 24/7 mission-critical relational database with high RAM requirements that demands uninterrupted execution and high availability.
Which TWO deployment and machine type strategies should the engineer select to meet performance requirements while minimizing costs? (Select TWO.)
- Provision Spot VMs for Workload 1 to significantly reduce compute expenses while accommodating instance preemptions.Cevap
- Provision memory-optimized standard VMs with Committed Use Discounts (CUDs) for Workload 2.Cevap
- CProvision Spot VMs for Workload 2 to lower the cost of memory-intensive database compute instances.
- DDeploy Workload 1 on Cloud Functions to run long-running batch jobs requiring custom third-party binary libraries and multi-hour execution windows.
Cevap
The architect should select Spot VMs for the fault-tolerant batch processing workload and memory-optimized standard VM instances with Committed Use Discounts for the 24/7 mission-critical database.
Spot VMs provide significant cost reductions for fault-tolerant, asynchronous batch jobs that tolerate preemption. For steady-state 24/7 high-memory database workloads, memory-optimized standard VMs secured with Committed Use Discounts (CUDs) provide guaranteed uptime alongside maximum cost optimization.
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Anahtar Kavram
Planning Compute Engine machine types, Spot VM suitability, and discount strategies based on workload SLA and resource profile