A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the…

While decryption is a necessary part of the process, stating it as a separate step after creating the Lambda function is redundant; decryption is an integral part of the 'processing' that the Lambda function would perform. The Lambda function, if correctly implemented, would encapsulate the decryption logic within its code.

An AWS Lambda function can be configured to subscribe to a DynamoDB stream, capture real-time item-level changes, and apply custom processing logic. For data encrypted with the DynamoDB encryption client, the Lambda function can be equipped with the necessary KMS permissions and client library to decrypt the sensitive data before transforming and loading it into Amazon Redshift, thus making it available for aggregation.

Amazon EMR is typically used for large-scale batch processing and analytics, not for real-time or near real-time stream processing from DynamoDB. While EMR can access KMS, it's not the most efficient or common solution for continuously ingesting and transforming a DynamoDB stream into Redshift.

Similar to option C, Amazon EMR is primarily a batch processing framework. Creating Apache Hive tables references data in S3 but doesn't address the real-time decryption and loading into Amazon Redshift from a DynamoDB stream, which is the core requirement for efficiency and security with client-side encrypted data.