Why is the ML Specialist not seeing the instance visible in the VPC?

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance’s Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 instance within the VPC.

Why is the ML Specialist not seeing the instance visible in the VPC?
A . Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
B . Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
C . Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
D . Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

View Answer

Answer: C

Explanation:

Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance’s EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the

customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases