AWS Certified AI Practitioner Practice Exam

What solution should be used to manage the flow of data from Amazon S3 to Amazon SageMaker Studio notebooks?

1. Use Amazon Inspector to monitor SageMaker Studio

2. Use Amazon Macie to monitor SageMaker Studio

3. Configure SageMaker to use a VPC with an S3 endpoint

4. Configure SageMaker to use S3 Glacier Deep Archive

The correct answer is: Configure SageMaker to use a VPC with an S3 endpoint

Managing the flow of data from Amazon S3 to Amazon SageMaker Studio notebooks can be efficiently achieved by configuring SageMaker to use a Virtual Private Cloud (VPC) with an S3 endpoint. This solution allows for secure and efficient data retrieval from S3 while providing network isolation. When SageMaker Studio is configured to operate within a VPC, it ensures that the communication between the SageMaker resources and S3 occurs over private IP addresses rather than over the public internet. This not only enhances security by keeping the data transfer private but also may improve performance due to the reduced latency often associated with private connections. The S3 VPC endpoint enables SageMaker to directly access S3 objects without needing to traverse the internet, which is essential for moving large datasets or models during machine learning workflows. Using a VPC provides added layers of protection and control over the data flow between resources, making it a central piece in configuring environments for machine learning tasks that leverage data stored in S3. In contrast to the other options, monitoring solutions like Amazon Inspector and Amazon Macie are primarily focused on security assessments and data privacy, respectively. Their roles do not directly address the data management and flow aspect between S3 and SageMaker notebooks. Configuring SageMaker to use S