AWS Certified Data Engineer Associate DEA-C01 Practice Question
You are building an order-tracking microservice that must persist 5-KB JSON order events. Expected traffic is 1,000 writes/sec and 3,000 strongly consistent reads/sec, but the workload can spike to 10× that level for short periods. You want to minimize cost during off-peak hours and avoid throttling without manually adjusting throughput. Which solution best meets these requirements?
Store events in an Amazon RDS MySQL database and add read replicas to absorb read traffic spikes.
Create a DynamoDB table in on-demand capacity mode and add a global secondary index on customerId.
Send events to an Amazon Kinesis Data Stream and periodically batch-load them into Amazon S3 for querying with Athena.
Create a DynamoDB table with 10,000 WCU and 30,000 RCU in provisioned mode and enable auto scaling.
DynamoDB on-demand capacity mode automatically scales up to handle sudden bursts and scales down when traffic subsides, so no manual capacity planning is required. Billing is per request, which keeps costs low during quieter periods, and a global secondary index can be added for customerId queries. Provisioned capacity with auto scaling still needs initial throughput settings and may throttle during sharp spikes. Amazon RDS requires instance sizing and replicas, making rapid burst handling and cost efficiency harder. Kinesis plus S3/Athena stores data cheaply but does not provide the millisecond read/write latency the microservice needs.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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