AWS Certified Data Engineer Associate DEA-C01 Practice Question
An analytics team must orchestrate a nightly pipeline: wait for an S3 file, run an AWS Glue job, load data into Amazon Redshift, then send an alert on failure. They want to define the workflow in Python, configure per-task retries, and monitor executions in a managed web UI. Which AWS solution best satisfies these needs?
Use AWS Data Pipeline to schedule the Glue job, Redshift COPY operation, and SNS activity with retry logic.
Build the workflow in AWS Step Functions with Lambda functions for each step and an SNS publish on failure.
Create a DAG in Amazon MWAA that uses an S3KeySensor, GlueJobOperator, RedshiftDataOperator, and an SNS notification task.
Set up an Amazon EventBridge cron rule that triggers an AWS Glue Workflow chaining the Glue job, Redshift COPY command, and SNS topic.
Amazon Managed Workflows for Apache Airflow (MWAA) lets engineers author DAGs in Python and provides Airflow operators such as S3KeySensor, GlueJobOperator, and RedshiftDataOperator. Each task can be given its own retry policy, and MWAA supplies a hosted Airflow UI for run history and manual retries, removing the need to manage Airflow infrastructure.
Step Functions could orchestrate the steps, but workflows are defined in Amazon States Language JSON/YAML, not Python, and it lacks built-in file-presence sensors. Glue Workflows and EventBridge rules chain Glue actions but cannot natively sense new S3 objects or mix non-Glue tasks without extra code. AWS Data Pipeline is a legacy service with limited Redshift operators and no Python DAG authoring or modern UI, so it does not meet all requirements.
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What is a DAG in the context of Apache Airflow?
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What is the purpose of the S3KeySensor in Amazon MWAA?
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How does MWAA differ from AWS Step Functions for this use case?
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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