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A retail company is developing a data lake solution on Amazon S3 to analyze historical...

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A retail company is developing a data lake solution on Amazon S3 to analyze historical sales data. The solution needs to support frequent schema changes as new product attributes are added. The company must also be able to query point-in-time historical data snapshots for compliance reporting. The solution must provide atomicity, consistency, isolation, and durability (ACID) transaction guarantees for concurrent write operations.

Which solution will meet these requirements?

A.

Create an AWS Glue Data Catalog table that uses CSV format. Schedule AWS Glue extract, transform, and load (ETL) jobs to transform the data into Parquet format and partition by date.

B.

Create an AWS Glue Data Catalog table that uses Apache Iceberg table format. Set the format version to 2. Configure time travel retention policies in the table properties.

C.

Enable Amazon S3 Versioning on the company ' s S3 bucket. Create an AWS Glue crawler to catalog the data. Use AWS Glue extract, transform, and load (ETL) jobs to read specific S3 version IDs.

D.

Store the data in Amazon DynamoDB with a composite primary key that includes a timestamp. Use Amazon DynamoDB Streams to capture changes and replicate to Amazon S3 in Parquet format.

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