AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineer must orchestrate an ETL pipeline that loads log files into Amazon Redshift. Whenever an object with the prefix "prod/logs/" is uploaded to an Amazon S3 bucket, the pipeline should start immediately. The solution must be fully serverless, offer built-in retry and dead-letter handling, and let the team add additional environment prefixes later without changing code. Which approach meets these requirements with the least operational effort?
Enable Amazon S3 EventBridge notifications; create an EventBridge rule that matches the prod/logs/ object-key prefix and invokes an AWS Step Functions state machine that runs the ETL workflow with an SQS dead-letter queue attached.
Create a CloudWatch Events schedule that runs every five minutes and calls a Lambda function to list the bucket for new objects and start the ETL job.
Configure an AWS Glue crawler trigger set to On-demand and launch it from a Lambda function that is invoked by S3 Event Notifications.
Set up S3 Event Notifications to invoke the ETL Lambda function directly; implement custom retry and environment filtering logic inside the function.
Amazon S3 can emit object-level events directly to the default Amazon EventBridge bus. A rule can filter on the object-key prefix (for example, prod/logs/*) and invoke an AWS Step Functions state machine as the target. EventBridge provides declarative retry policies and can route failed invocations to an Amazon SQS dead-letter queue, satisfying resiliency requirements without custom code. Because filtering is handled in the rule, adding more environments only requires creating or updating rules, not modifying Lambda or ETL code. The other options either rely on polling, lack built-in DLQ support, or require embedding filtering and retry logic inside application code, increasing operational overhead.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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