A team of data scientists is using Amazon SageMaker instances and SageMaker APIs to train machine learning (ML) models. The SageMaker instances are deployed in a VPC that does not have access to or from the internet. Datasets for ML model training are stored in an Amazon S3 bucket. Interface VPC endpoints provide access to Amazon S3 and the SageMaker APIs. Occasionally, the data scientists require access to the Python Package Index (PyPI) repository to update Python packages that they use as part of their work flow. A solutions architect must provide access to the PyPI repository while ensuring that the SageMaker instances remain isolated from the internet. Which solution will meet these requirements?

QuestionsCategory: SAP-C02A team of data scientists is using Amazon SageMaker instances and SageMaker APIs to train machine learning (ML) models. The SageMaker instances are deployed in a VPC that does not have access to or from the internet. Datasets for ML model training are stored in an Amazon S3 bucket. Interface VPC endpoints provide access to Amazon S3 and the SageMaker APIs. Occasionally, the data scientists require access to the Python Package Index (PyPI) repository to update Python packages that they use as part of their work flow. A solutions architect must provide access to the PyPI repository while ensuring that the SageMaker instances remain isolated from the internet. Which solution will meet these requirements?
Admin Staff asked 10 months ago
A team of data scientists is using Amazon SageMaker instances and SageMaker APIs to train machine learning (ML) models. The SageMaker instances are deployed in a VPC that does not have access to or from the internet. Datasets for ML model training are stored in an Amazon S3 bucket. Interface VPC endpoints provide access to Amazon S3 and the SageMaker APIs.
Occasionally, the data scientists require access to the Python Package Index (PyPI) repository to update Python packages that they use as part of their work flow. A solutions architect must provide access to the PyPI repository while ensuring that the SageMaker instances remain isolated from the internet.
Which solution will meet these requirements?

A. Create an AWS CodeCommit repository for each package that the data scientists need to access. configure code synchronization between the PyPI repository and the CodeCommit repository. Create a VPC endpoint for CodeCommit.

B. Create a NAT gateway in the VP

C. configure VPC routes to allow access to the internet with a network ACL that allows access to only the PyPI repository endpoint.

D. Create a NAT instance in the VPconfigure VPC routes to allow access to the internet. configure SageMaker notebook instance firewall rules that allow access to only the PyPI repository endpoint.

E. Create an AWS CodeArtifact domain and repository. Add an external connection for public:pypi to the CodeArtifact repository. configure the Python client to use the CodeArtifact repository. Create a VPC endpoint for CodeArtifact.


 
Correct Answer: D

This question is in SAP-C02 exam
For getting AWS Certified Solutions Architect Professional Certificate


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