AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ269

Data Operations and Support

A company receives a daily customer-data file in .xls format and stores it in Amazon S3. Each daily file is approximately 2 GB.

A data engineer concatenates the file’s customer first-name column with its customer last-name column. The data engineer must determine the number of distinct customers in the file.

Which solution meets this requirement with the LEAST operational effort?

  • A Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
  • B Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
  • C Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
  • D Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
Explanation

AWS Glue DataBrew provides a visual, code-free recipe interface with built-in aggregate functions like COUNT_DISTINCT. A DataBrew recipe can concatenate customer first and last names and then apply COUNT_DISTINCT to calculate unique customers, all without writing Apache Spark code or maintaining an ETL pipeline. This is the lowest-effort solution for simple data preparation and analysis tasks.

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