Users upload small files into Amazon S3. After upload each file requires a one-time simple transformation into JSON for later analysis. Each file must be processed as quickly as possible after upload. Demand varies widely from high to low or none. Which solution meets these requirements with the LEAST operational overhead?

The correct solution leverages S3 event notifications to an SQS queue, triggering an AWS Lambda function for processing, and storing results in DynamoDB. This architecture provides the least operational overhead because S3 event notifications directly integrate with SQS, and Lambda is a serverless compute service that automatically scales with demand, eliminating the need to manage servers. DynamoDB is a fully managed NoSQL database, also offering high scalability and low operational overhead. Incorrect options: EMR involves managing clusters, which has higher operational overhead. Aurora is a relational database, potentially less suitable for simple key-value JSON storage compared to DynamoDB, and has higher operational overhead than DynamoDB for this use case. Using EC2 instances requires managing servers, scaling them manually or with Auto Scaling Groups, which is more operational overhead than serverless Lambda functions. EventBridge to Kinesis Data Streams adds unnecessary complexity and latency for a simple file upload event. Kinesis is typically used for real-time streaming data, not discrete file uploads. Aurora also adds unnecessary complexity and operational overhead compared to DynamoDB for this use case.