AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ252

Data Ingestion and Transformation

A company receives data from multiple sources and stores it in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3-based data lake. The company uses Amazon Athena to query the data in the data lake.

The company must identify matching records even if the records lack a common unique identifier.

Which solution meets this requirement?

  • A Use Amazon Macie pattern matching as part of the ETL job.
  • B Train and use the AWS Glue PySpark Filter class in the ETL job.
  • C Partition tables and use the ETL job to partition the data on a unique identifier.
  • D Train and use the AWS Lake Formation FindMatches transform in the ETL job.
Explanation

AWS Lake Formation FindMatches is a machine learning transform that is trained with labeled examples to identify duplicate or matching records, including when records have no common unique identifier and no fields match exactly. It can be invoked from a Spark-based AWS Glue ETL job.

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