AWS Certified Data Engineer Associate DEA-C01 Practice Question
A DynamoDB table that stores IoT sensor readings peaks at 40,000 writes per second. The analytics team must land every new item in an Amazon S3 data lake within 60 seconds. The solution must auto-scale, provide at-least-once delivery, and minimize operational overhead. Which architecture meets these requirements MOST effectively?
Create an AWS Glue streaming ETL job that consumes the table's stream ARN directly and writes the data to Amazon S3.
Use AWS Database Migration Service in change data capture mode to replicate the DynamoDB table continuously to an S3 target.
Enable DynamoDB Streams with the NEW_IMAGE view and configure an AWS Lambda function as the event source; inside the function batch the records and submit them to an Amazon Kinesis Data Firehose delivery stream that writes to S3.
Schedule an AWS Glue batch job every minute to export the entire table to S3 by using DynamoDB export to S3.
Using DynamoDB Streams with an AWS Lambda function satisfies all stated requirements. Streams delivers every write in near-real time. Lambda can be configured as a native event source and automatically scales with the number of stream shards, giving at-least-once processing without server management. The function can batch the records and invoke an Amazon Kinesis Data Firehose delivery stream, which efficiently loads the data into Amazon S3. Glue streaming jobs cannot consume DynamoDB Streams, DMS introduces additional infrastructure and higher latencies, and frequent full-table exports are inefficient and would miss the one-minute latency target.
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What is DynamoDB Streams with NEW_IMAGE view?
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How does AWS Lambda integrate with DynamoDB Streams?
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What is Amazon Kinesis Data Firehose, and how does it work with S3?
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What is DynamoDB Streams?
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What is the role of AWS Lambda in this solution?
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How does Amazon Kinesis Data Firehose integrate with S3?
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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