DEA-C01 Question #5: Real Exam Question with Answer & Explanation

Question

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour. Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

Options

• AConfigure AWS Glue triggers to run the ETL jobs every hour.
• BUse AWS Glue DataBrew to clean and prepare the data for analytics.
• CUse AWS Lambda functions to schedule and run the ETL jobs every hour.
• DUse AWS Glue connections to establish connectivity between the data sources and Amazon
• EUse the Redshift Data API to load transformed data into Amazon Redshift.

Explanation

AWS Glue triggers provide a simple and integrated way to schedule ETL jobs. By configuring these triggers to run hourly, the data engineer can ensure that the data processing and updates occur as required without the need for external scheduling tools or custom scripts. This approach is directly integrated with AWS Glue, reducing the complexity and operational overhead. AWS Glue supports connections to various data sources, including Amazon RDS and MongoDB. By using AWS Glue connections, the data engineer can easily configure and manage the connectivity between these data sources and Amazon Redshift. This method leverages AWS Glue’s built-in capabilities for data source integration, thus minimizing operational complexity and ensuring a seamless data flow from the sources to the destination (Amazon Redshift).

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