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Answer: Implement Amazon Kinesis Data Streams for transaction streaming, AWS Lambda for sensitive data removal, and Amazon DynamoDB for secure data storage with stream availability for other applications.
The recommended approach is to use Amazon Kinesis Data Streams for transaction streaming and AWS Lambda for processing and removing sensitive data on the fly, before storing the sanitized data in Amazon DynamoDB. This ensures scalable, near-real-time processing and low-latency retrieval of transactions, while allowing internal applications to consume the data directly from the Kinesis data stream. Option C effectively meets all the requirements of scalable processing, sensitive data removal, and data sharing with internal applications.
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An AWS-hosted online marketplace requires a scalable, near-real-time solution to process millions of financial transactions, removing sensitive data, and sharing details with internal applications. What is the recommended approach for a solutions architect?
A
Utilize Amazon DynamoDB for storage, apply a rule to filter sensitive data during writes, and employ DynamoDB Streams for data sharing.
B
Leverage Amazon Kinesis Data Firehose to stream and store data in DynamoDB and S3, integrate AWS Lambda for data sanitization, and enable S3 as the data consumption source for applications.
C
Implement Amazon Kinesis Data Streams for transaction streaming, AWS Lambda for sensitive data removal, and Amazon DynamoDB for secure data storage with stream availability for other applications.
D
Use Amazon S3 to batch and store transaction files, apply AWS Lambda for processing and sensitive data removal, then update S3 and store sanitized data in DynamoDB for application access.