AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests high-frequency IoT sensor readings and must land them in Amazon S3 in under 30 seconds. Operations teams also need the ability to replay any portion of the incoming stream if a downstream transformation job fails. Which solution meets these requirements while keeping operational overhead to a minimum?
Use an AWS IoT Core rule to write the sensor messages directly to an S3 bucket with the "Customer managed" retry option enabled.
Send the data to Amazon Kinesis Data Streams with a 24-hour retention period, add an Amazon Kinesis Data Firehose delivery stream as a consumer, and configure Firehose buffering to 1 MiB or 10 seconds before writing to Amazon S3.
Create a direct Amazon Kinesis Data Firehose delivery stream and reduce the S3 buffering size to 1 MiB and interval to 10 seconds.
Deploy an Apache Kafka cluster on Amazon EC2, configure a topic for the sensors, and use a Kafka Connect S3 sink to write data to Amazon S3 every 10 seconds.
Amazon Kinesis Data Streams provides a durable stream that can retain data for hours or days, allowing applications to reread any record for replay or recovery. By registering Amazon Kinesis Data Firehose as a stream consumer and setting the buffering limits to 1 MiB and 10 seconds, records are delivered to Amazon S3 in well under the 30-second requirement. A Firehose delivery stream that ingests data directly, an AWS IoT Core rule, or a self-managed Kafka cluster either lacks built-in replay capability or adds unnecessary operational burden, so they do not satisfy both requirements as effectively.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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