AWS Certified Data Engineer Associate DEA-C01 Practice Question
An e-commerce company must migrate its 15-TB/day Apache Kafka workload to AWS. Producers and consumers already use native Kafka APIs and several Kafka Connect connectors. Traffic peaks are unpredictable. The team wants to avoid application changes, keep 7-day topic retention, and pay only for throughput actually used while the service scales automatically. Which AWS solution best meets these cost and performance requirements?
Rewrite producers and consumers to use Amazon Kinesis Data Streams in on-demand capacity mode.
Replace Kafka with Amazon SQS FIFO queues and enable long polling to handle burst traffic.
Run the workload on Amazon MSK Serverless and keep the default 7-day retention for each topic.
Send events directly to Amazon S3 through Amazon Kinesis Data Firehose and query them with Amazon Athena.
Amazon MSK Serverless provides fully managed, automatically scaling Apache Kafka clusters that are compatible with existing Kafka producers, consumers, and Kafka Connect connectors. The service retains all Kafka capabilities (including configurable topic retention) and charges primarily for the data ingested, stored, and streamed, so there is no need to provision broker instances for peak load. Kinesis Data Streams, SQS, and Kinesis Data Firehose would require rewriting clients or do not offer full Kafka feature parity, making them unsuitable given the stated constraints.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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