AWS Certified Solutions Architect Professional SAP-C02 Practice Question
A travel-booking platform stores short-lived user session data in an Amazon RDS for MySQL database that also holds transactional booking information. During flash-sale events, the volume of session traffic can spike unpredictably to 100,000 read and write operations per second, overwhelming the relational instance. Each session record is smaller than 4 KB, identified by a unique SessionID, and must expire automatically 30 minutes after it is created.
The modernization team wants to migrate this session workload to a purpose-built AWS database service that will:
Provide sub-10-millisecond read and write latency at any scale
Scale automatically to accommodate traffic spikes without capacity planning
Delete expired session records automatically without additional application code
Bill only for actual read and write requests during off-peak periods
Require minimal operational management effort
Which solution meets these requirements MOST cost-effectively?
Migrate the session store to an Amazon ElastiCache for Redis cluster with cluster-mode enabled and set a 30-minute key expiration for each session.
Create an Amazon DynamoDB table, enable on-demand capacity mode, and configure a Time To Live (TTL) attribute set to 30 minutes for each session item.
Create an Amazon Redshift Serverless workgroup and configure automatic vacuum and retention policies to remove session rows after 30 minutes.
Deploy an Amazon Aurora Serverless v2 MySQL cluster and schedule an hourly SQL event to purge session rows older than 30 minutes.
An Amazon DynamoDB table in on-demand capacity mode delivers single-digit millisecond latency, automatically scales from zero to millions of requests per second without manual capacity planning, and charges on a pay-per-request basis. Enabling a TTL attribute on the table causes DynamoDB to delete items after the configured 30-minute expiration time, eliminating the need for custom purge logic.
ElastiCache for Redis can meet the latency target but relies on in-memory nodes that must be sized and managed, making it more expensive at scale and not pay-per-request. Aurora Serverless v2 still incurs relational overhead, does not natively expire rows, and may struggle to sustain 100 K TPS without manual tuning. Redshift Serverless is optimized for analytics rather than high-velocity key-value access and cannot meet the required per-request latency.
Therefore, DynamoDB with on-demand capacity mode and TTL is the most cost-effective, fully managed solution for this session-state modernization.
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