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A company ingests data from multiple data sources and stores the data in an Amazon...

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A company ingests data from multiple data sources and stores the data 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 that is in the data lake.

The company needs to identify matching records even when the records do not have a common unique identifier.

Which solution will meet this requirement?

A.

Use Amazon Made 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.

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