AWS Certified Data Engineer Associate DEA-C01 Practice Question
A sports analytics company collects player telemetry from mobile devices at a steady rate of 4 000 records/sec, with unpredictable bursts up to 25 000 records/sec for short periods. Ingested data must arrive in Amazon S3 within 60 seconds and must be replayable for 24 hours if downstream processing fails. Which ingestion solution meets the requirements most cost-effectively?
Use an Amazon Kinesis Data Streams on-demand stream and invoke an AWS Lambda function to write batches of records to Amazon S3.
Create a provisioned Amazon Kinesis Data Streams stream with 25 shards and configure Kinesis Data Firehose to deliver records to Amazon S3.
Deploy an Amazon MSK cluster with t3.small brokers and use Kafka Connect with an S3 sink connector to store the records.
Set up an AWS DMS continuous CDC task that ingests the telemetry stream and uses a Kinesis target to deliver data to Amazon S3.
Kinesis Data Streams in on-demand mode automatically scales shard capacity up and down based on traffic, so the company pays only for the throughput that is actually used during brief spikes instead of permanently provisioning extra shards. A Lambda function invoked by the stream can buffer records and write them to Amazon S3, achieving sub-minute end-to-end latency. Kinesis Data Streams retains data for 24 hours by default, allowing the telemetry to be replayed if needed.
A provisioned stream would need about 25 shards to sustain the 25 000-records/sec peak, so the business would pay for that capacity even when traffic is at the normal 4 000-records/sec level, making it more expensive overall. Running an Amazon MSK cluster introduces higher operational overhead and cost for this throughput, and AWS DMS is designed for database change capture, not high-frequency mobile telemetry ingestion.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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