Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

A startup collects 500 GB per day of IoT sensor readings in nested JSON and keeps relational product metadata in Amazon Redshift. Cost control rules out continuously loading the raw sensor files into Redshift. Analysts need to join the sensor data with the product tables and run ad-hoc queries that must stay performant as the sensor schema evolves. Which approach best models these structured and semi-structured datasets to meet the requirements?

  • Store sensor data as compressed Parquet files in Amazon S3, register the files with the AWS Glue Data Catalog, and query them from Redshift using Spectrum while keeping product metadata in native Redshift tables.

  • Keep the sensor files as raw JSON objects in S3, query them with Amazon Athena, and export nightly query results to Redshift for joins with the product tables.

  • Load both the JSON sensor data and the product metadata into Redshift tables that use the SUPER data type and late-binding views to handle nested attributes.

  • Persist the sensor data in Amazon DynamoDB and access it from Redshift with federated queries while maintaining the product metadata in DynamoDB global tables.

AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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