A company is designing a new web application and must select database options that minimize costs while meeting performance and availability requirements. The application has two primary database workloads:
1. A user session data store that experiences highly unpredictable, spiky traffic throughout the day with long periods of complete inactivity.
2. A relational transaction database that requires automatic failover across multiple Availability Zones, but has low, variable usage that drops significantly at night.
Which two database configurations should a solutions architect recommend to meet these requirements most cost-effectively? (Select TWO.)
- Deploy an Amazon DynamoDB table using on-demand capacity mode for the user session store.Cevap
- Deploy Amazon Aurora Serverless v2 in a Multi-AZ configuration for the relational database.Cevap
- CDeploy an Amazon DynamoDB table using provisioned capacity mode with auto scaling for the user session store.
- DDeploy a single-AZ Amazon RDS DB instance for the relational database, and configure a Read Replica in a separate Availability Zone to act as the primary failover target.
- EDeploy an Amazon RDS DB instance in a Multi-AZ configuration, and purchase a Compute Savings Plan to reduce compute costs.
Cevap
The correct configurations are deploying Amazon DynamoDB in on-demand capacity mode for the user session store and deploying Amazon Aurora Serverless v2 in a Multi-AZ configuration for the relational database.
On-demand capacity mode for Amazon DynamoDB is highly cost-effective for unpredictable, spiky workloads because it charges per request without requiring provisioned capacity that would be wasted during idle times. Amazon Aurora Serverless v2 automatically scales compute capacity to match application demand, making it ideal for variable workloads that drop off at night, and when deployed in Multi-AZ, it provides the required automatic failover.
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Anahtar Kavram
Selecting the cost-optimal capacity scaling mechanism (on-demand vs. provisioned vs. serverless scaling) and high-availability options based on workload patterns.