Question
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon S3 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
- B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
- C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data. Correct
- D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
Explanation
Amazon Macie is specifically designed to automatically discover, classify, and protect sensitive data in AWS environments, making it the most efficient choice for identifying sensitive data with minimal operational overhead. Using AWS Lambda functions alongside Macie further reduces operational complexity by automating the process of removing identified sensitive data without requiring the management of servers or clusters.
