A data science team will store many different training datasets and must support automatic scaling, cost efficiency, and SQL-based exploration. Which storage approach is best suited for this use case?

Storing datasets as files in Amazon S3 is the best choice because S3 offers automatic scaling, cost-effective storage for virtually unlimited data, and integrates seamlessly with services like Amazon Athena for SQL-based exploration. Athena allows you to query data directly in S3 using standard SQL without needing to load it into a database. Storing on an Amazon EBS volume attached to an EC2 instance is less suitable because EBS volumes have fixed sizes, requiring manual scaling, and are not inherently cost-efficient for large, infrequently accessed datasets. Keeping datasets in a multi-node Amazon Redshift cluster is expensive for just storing raw training data and is optimized for analytical queries on structured data, not raw file storage. Storing datasets as global tables in Amazon DynamoDB is designed for NoSQL key-value and document data, not for large files or SQL-based exploration of file-based datasets.