AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ30

Data Ingestion and Transformation

A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database. Historical sales analytics are maintained in Amazon Redshift. The company must create daily reports that combine the current day’s transactions with historical sales patterns for trend analysis. The solution must deliver near-real-time insights while minimizing data-transfer costs and maintenance overhead.

Which solution meets these requirements?

  • A Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
  • B Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.
  • C Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
  • D Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.
Explanation

Amazon Redshift federated queries can query live data in Amazon RDS for MySQL and join it with data stored in Amazon Redshift. This makes current operational transactions available for reporting without first replicating or loading them into Redshift, which supports near-real-time analysis while avoiding the data movement and ongoing pipeline maintenance of DMS, ETL, or S3-based loading.

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