An ML engineer must process thousands of existing CSV files plus new CSV uploads stored in a central S3 bucket. All CSVs share the same column layout and include a transaction date column that must be queried. Which solution delivers this with the least operational overhead?

The correct option leverages Amazon Athena, a serverless query service, to directly query data in S3. Using CREATE TABLE AS SELECT (CTAS) allows you to define a table over your existing CSV files, specifying partitioning by transaction date. This significantly reduces operational overhead as there are no servers to manage, and Athena handles the schema-on-read for your CSVs. You can then query this table efficiently. Incorrect options: Replicating objects to a new S3 bucket and using S3 Object Lambda adds unnecessary data movement and complexity for querying. S3 Object Lambda is for transforming data during retrieval, not primarily for defining queryable structures over existing files. Running an AWS Glue for Apache Spark job to move and query data introduces a managed Spark environment, which has more operational overhead than Athena for simple querying of existing CSVs. Using Amazon Kinesis Data Firehose with a Lambda function is designed for streaming data ingestion and transformation, not for querying existing static files in S3. It would involve moving all existing data, which is inefficient.