A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL. Which solution will meet these requirements with the LEAST operational overhead?

Topic #: - All AWS Certified Data Engineer - Associate DEA-C01 Questions

A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
D. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

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