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AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company stores transactional data in Amazon RDS for PostgreSQL. Each night, analysts need the entire contents of the orders table in Amazon S3 as partitioned Parquet files so that Amazon Athena queries run efficiently. The data engineering team wants a fully managed, serverless solution that requires little code and can be scheduled to run automatically at midnight. Which approach meets these requirements while minimizing operational overhead?

  • Create an AWS Glue ETL job that uses a JDBC connection to the RDS database, converts the orders table to Parquet, writes it to an S3 prefix partitioned by date, and schedule the job with an AWS Glue trigger.

  • Take an automated PostgreSQL snapshot each night and copy the snapshot files directly to an S3 bucket for Athena to query.

  • Use AWS Data Pipeline to run a nightly Amazon EC2 task that exports the table to CSV files and uploads them to S3.

  • Configure AWS Database Migration Service for a full-load task that sends data through Amazon Kinesis Data Firehose to S3 in Parquet format.

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