SCS-C02 · Question #144
A company is developing a mechanism that will help data scientists use Amazon SageMaker to read, process, and output data to an Amazon S3 bucket. Data scientists will have access to a dedicated S3 pre
The correct answer is C. Create an S3 Lifecycle configuration for each S3 bucket prefix for each project. Set the S3. https://docs.aws.amazon.com/AmazonS3/latest/userguide/lifecycle-configuration- examples.html#lifecycle-config-conceptual-ex3
Question
A company is developing a mechanism that will help data scientists use Amazon SageMaker to read, process, and output data to an Amazon S3 bucket. Data scientists will have access to a dedicated S3 prefix for each of their projects. The company will implement bucket policies that use the dedicated S3 prefixes to restrict access to the S3 objects. The projects can last up to 60 days. The company's security team mandates that data cannot remain in the S3 bucket after the end of the projects that use the data. Which solution will meet these requirements MOST cost-effectively?
Options
- ACreate an AWS Lambda function to identify and delete objects in the S3 bucket that have not
- BCreate a new S3 bucket. Configure the new S3 bucket to use S3 Intelligent-Tiering. Copy the
- CCreate an S3 Lifecycle configuration for each S3 bucket prefix for each project. Set the S3
- DCreate an AWS Lambda function to delete objects that have not been accessed for 60 days.
How the community answered
(32 responses)- A16% (5)
- B3% (1)
- C75% (24)
- D6% (2)
Explanation
https://docs.aws.amazon.com/AmazonS3/latest/userguide/lifecycle-configuration- examples.html#lifecycle-config-conceptual-ex3
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