A financial technology company is designing the Google Cloud Compute Engine resource strategy for two distinct backend services:
1. A fault-tolerant, stateless batch risk analytics service that processes asynchronous job queues.
2. A mission-critical relational database service requiring continuous 24/7 availability with predictable capacity.
Which of the following compute provisioning strategies should the cloud engineer implement? (Select TWO.)
- Provision Spot VMs for the fault-tolerant batch risk analytics workload to minimize compute costs.Answer
- Purchase Committed Use Discounts (CUDs) for the vCPU and memory footprint of the continuous relational database workload.Answer
- CDeploy the mission-critical relational database on Spot VMs to optimize cost efficiency during off-peak hours.
- DRely on automatic Sustained Use Discounts (SUDs) instead of Committed Use Discounts to maximize savings for the 24/7 database workload.
Answer
Provision Spot VMs for the fault-tolerant batch risk analytics workload, and purchase Committed Use Discounts (CUDs) for the continuous relational database workload.
The correct strategy combines Spot VMs for the fault-tolerant risk analytics workload and Committed Use Discounts (CUDs) for the 24/7 database. Spot VMs lower compute costs substantially for stateless workloads that tolerate preemption. For steady 24/7 database workloads, purchasing a 1-year or 3-year Committed Use Discount yields maximum cost savings while preserving continuous instance availability.
Step-by-Step Solution
Key Concept
Compute Engine Machine Provisioning and Pricing Models (Spot VMs vs. Committed Use Discounts)