AWS Certified Data Engineer Associate DEA-C01 Practice Question
Your company runs an Amazon MWAA environment to orchestrate an hourly batch ingestion from an on-premises database to Amazon S3, followed by a load into Amazon Redshift. The pipeline must start exactly at the top of each hour, skip any overdue runs when the previous run is still in progress, and keep only one active run at a time. Which Apache Airflow configuration best satisfies these scheduling requirements?
Set schedule_interval=None and configure Amazon EventBridge to invoke the DAG every hour via the MWAA API.
Define the DAG with schedule_interval="0 * * * *", set concurrency=1 and max_active_runs=1, and specify catchup=False.
Trigger the pipeline from an upstream DAG with TriggerDagRunOperator and leave catchup=True so any missed hours are rerun automatically.
Use schedule_interval="@hourly" and set depends_on_past=True in default_args so that a new run waits for the previous one to finish.
Setting a cron expression of "0 * * * *" guarantees that the DAG is triggered on the hour. Setting both concurrency and max_active_runs to 1 ensures that only one task instance and one DAG run can execute simultaneously, preventing overlaps. Finally, catchup=False instructs Airflow to ignore any runs that were missed while a previous run was still executing, so no backlog is created. The other options either allow overlapping runs, rely on catch-up processing, or disable the built-in scheduler entirely, so they do not meet all stated constraints.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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