A company is planning to deploy a new web application that consists of a 24/7 continuous backend API and a separate daily batch data processing job that is fault-tolerant and can be run at any time. The solutions architect needs to design a cost-optimized compute strategy for these workloads. Which combination of compute selection and purchasing strategies will meet these requirements at the lowest cost? (Select TWO.)
- Commit to a Compute Savings Plan to cover the continuous 24/7 backend API baseline.Cevap
- Utilize Amazon EC2 Spot Instances to run the daily batch data processing jobs.Cevap
- CCommit to a Compute Savings Plan to cover both the compute instances and the Amazon RDS database storage costs.
- DDeploy the daily batch data processing jobs on AWS Lambda with a 15-minute timeout configuration to run continuous background processing.
- EPurchase a Compute Savings Plan to discount the storage capacity of the Amazon DynamoDB table used by the application.
Cevap
Commit to a Compute Savings Plan to cover the continuous 24/7 backend API baseline, and utilize Amazon EC2 Spot Instances to run the daily batch data processing jobs.
The correct strategy combines a Compute Savings Plan to cover the continuous, predictable 24/7 backend API baseline with EC2 Spot Instances for the daily, fault-tolerant batch processing. This leverages the high discount of Spot Instances (up to 90%) for flexible workloads while securing a baseline discount (up to 66%) for the steady-state API component.
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Selecting the most cost-effective compute purchasing models (Savings Plans vs Spot Instances) based on workload predictability and fault tolerance.
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