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AWS Certified Data Engineer Associate DEA-C01 Practice Question

A data engineering team streams records with Amazon Kinesis Data Firehose into an S3 bucket. When each hourly object arrives, the team must start an AWS Glue Spark job to cleanse the data and then run a SQL MERGE in Amazon Redshift. The orchestration should offer built-in retries, send one Amazon SNS alert on any failure, and require the least custom code. Which solution meets these requirements?

  • Deploy an Amazon Managed Workflows for Apache Airflow (MWAA) environment and build a DAG that polls the S3 bucket hourly, triggers the Glue job, executes the Redshift merge with a JDBC operator, and sends an SNS alert from an Airflow callback.

  • Create an Amazon EventBridge ObjectCreated rule that starts an AWS Step Functions state machine. Use the Glue StartJobRun task, then an AWS SDK task to call the Redshift Data API ExecuteStatement. Add a Catch path that publishes to SNS on any error.

  • Attach the S3 bucket event to a Lambda function that starts the Glue job, loops on GetJobRun until completion, then connects to Redshift with psycopg2 to run the merge; configure CloudWatch Alarms to forward errors to SNS.

  • Create an AWS Glue workflow triggered by S3 Event Notifications, add the ETL job, and include a Python shell step that runs the Redshift SQL; configure the workflow to publish an SNS message on failure.

AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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