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Answer: Leverage Macie to analyze the output of the daily batch job and look for any sensitive data findings of type SensitiveData:S3Object/Financial
Overall explanation Correct option: Leverage Macie to analyze the output of the daily batch job and look for any sensitive data findings of type SensitiveData:S3Object/Financial Amazon Macie is a data security service that discovers sensitive data by using machine learning and pattern matching, provides visibility into data security risks, and enables automated protection against those risks. To help you manage the security posture of your organization's Amazon Simple Storage Service (Amazon S3) data estate, Macie provides you with an inventory of your S3 buckets, and automatically evaluates and monitors the buckets for security and access control. If Macie detects a potential issue with the security or privacy of your data, such as a bucket that becomes publicly accessible, Macie generates a finding for you to review and remediate as necessary. Macie also automates the discovery and reporting of sensitive data to provide you with a better understanding of the data that your organization stores in Amazon S3. To detect sensitive data, you can use built-in criteria and techniques that Macie provides, custom criteria that you define, or a combination of the two. If Macie detects sensitive data in an S3 object, Macie generates a finding to notify you of the sensitive data that Macie found. Macie generates a sensitive data finding when it detects sensitive data in an S3 object that it analyzes to discover sensitive data. This includes analysis that Macie performs when you run a sensitive data discovery job and when it performs automated sensitive data discovery. For the given use case, you can use Macie to analyze the output of the daily batch job and look for any sensitive data findings of type SensitiveData:S3Object/Financial which implies that the S3 object contains financial information, such as bank account numbers or credit card numbers.
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A financial services company leverages Amazon S3 for the storage of transformed and anonymized customer data produced by a daily batch job. The development team has been assigned to create a solution to scrutinize the output of this daily batch job for any potentially sensitive financial information related to the company’s customers.
As an AWS Certified Developer Associate, which of the following options would you recommend to address this use case in the MOST efficient manner?
A
Leverage Macie to analyze the output of the daily batch job and look for any sensitive data findings of type SensitiveData:S3Object/CustomIdentifier
B
Leverage Macie to analyze the output of the daily batch job and look for any sensitive data findings of type SensitiveData:S3Object/Personal
C
Configure a S3 event notification for every object upload that triggers a Lambda function based Python script to detect sensitive customer information
D
Leverage Macie to analyze the output of the daily batch job and look for any sensitive data findings of type SensitiveData:S3Object/Financial