You work as a machine learning specialist for a security firm that requires you to encrypt all of your machine learning infrastructure in transit and at rest. Your team is building a fraud detection algorithm using the Random Cut Forest SageMaker built-in algorithm. You and your teammates are using SageMaker notebook instances to build your model components. You need to customize the operating system of your notebook instances by installing custom libraries and setting specific operating system level configurations to meet your firm’s security requirements. Your Chief Financial Officer wants to keep the cost of running your SageMaker instances as low as possible. Therefore, you are required to manage the runtime of your SageMaker notebook instances, only having them running when they are actively in use. How can you meet your requirements most efficiently?

Answer options:

A.Stop your notebook instances at the end of each day and start them up again at the beginning of the next workday. Your customizations to the operating system will be maintained across the stopping and starting of your instances.
B.Keep your SageMaker notebook instances running until your team has completed building your Random Cut Forest model.
C.Use a lifecycle configuration to automate customizations of your notebook instances, stopping the instances at the end of each workday and starting the instances at the beginning of each workday.
D.Terminate the SageMaker notebook instances at the end of each workday and recreate them at the start of each workday.

Correct Answer: C
Option A is incorrect. When you stop your SageMaker notebook instances, customizations to the operating system, such as installed custom libraries or operating system level settings, are lost.
Option B is incorrect. Keeping your SageMaker notebook instances running until your team has completed building your Random Cut Forest model will not meet your chief financial officer’s requirement of keeping the cost of running your SageMaker instances as low as possible.
Option C is correct. To avoid losing the installation of custom libraries and setting specific operating system level configurations, you can use a lifecycle configuration to automate customizations of your notebook instances.
Option D is incorrect. When you terminate your SageMaker notebook instances, the snapshot and the ML storage volume are deleted, thereby deleting your installation of custom libraries and setting specific operating system level configurations.
References:
Please see the Amazon SageMaker developer guide titled Random Cut Forest (RCF) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html),
The Amazon SageMaker developer guide titled Notebook instances and SageMaker jobs (https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest-nbi.html)