AWS Certified Solutions Architect Professional SAP-C02 Practice Question
A global retail company is modernizing its e-commerce platform, which currently relies on a large, self-managed PostgreSQL database running on a single Amazon EC2 instance. The platform experiences extreme traffic variations, with massive, unpredictable spikes during flash sales that require the database to be provisioned for peak capacity at all times. This leads to high costs during normal traffic periods and a significant operational burden on the database administration team for tasks like patching, scaling, and ensuring high availability.
The company wants to migrate to a fully managed, cost-effective database solution that preserves the existing relational schema to minimize application code changes. The solution must automatically scale compute capacity in response to the unpredictable traffic spikes and scale down during quiet periods to reduce costs.
Which solution should a solutions architect recommend to meet these requirements most effectively?
Migrate the database to Amazon Aurora PostgreSQL-Compatible Edition with an Aurora Serverless v2 writer instance.
Re-platform the application to use Amazon DynamoDB with on-demand capacity mode.
Continue running the PostgreSQL database on an Amazon EC2 instance but place it within an Auto Scaling group.
Migrate the database to Amazon Aurora PostgreSQL-Compatible Edition with a provisioned writer instance and Auto Scaling read replicas.
The correct answer is to migrate the database to Amazon Aurora PostgreSQL-Compatible Edition with an Aurora Serverless v2 writer instance. Amazon Aurora Serverless v2 is specifically designed for workloads with sudden and unpredictable changes in activity. It automatically and instantly scales compute and memory resources in fine-grained increments to match the application's load, without disrupting database connections. This addresses the core requirements of handling spiky traffic and minimizing costs by only paying for the capacity consumed. As a fully managed service compatible with PostgreSQL, it also eliminates the operational overhead of managing the database infrastructure while preserving the relational model.
Migrating to Amazon Aurora Provisioned is not the most optimal solution. While it reduces management overhead, the primary writer instance in a provisioned cluster has a fixed capacity. Scaling the writer requires manual intervention or a failover to a larger instance, which is not automatic and can incur downtime. This makes it less cost-effective for highly unpredictable workloads compared to Serverless v2.
Re-platforming to use Amazon DynamoDB would require a significant refactoring of the application, as it would mean moving from a relational (SQL) to a NoSQL data model. The scenario specifies a requirement to preserve the relational schema to minimize code changes, making this option unsuitable despite DynamoDB's excellent scaling capabilities.
Using an Amazon EC2 instance within an Auto Scaling group is not a recommended pattern for a primary stateful relational database. Managing state, data replication, and failover in such a configuration is extremely complex and error-prone, and it would not reduce the database administration burden. This pattern is better suited for stateless application tiers.
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