MLS-C01 · Question #141
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data…
The correct answer is A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. D. Enable network isolation for training jobs and models. E. Restrict notebook presigned URLs to specific IPs used by the company. As per official document only 4 ways to do data egress Enforcing deployment in VPC, Enforcing network isolation, Restricting notebook pre-signed URLs to IPs, Disabling internet access. Read Controlling data egress section Link…
Question
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment. Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)
Options
- AConnect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
- BUse SCPs to restrict access to SageMaker.
- CDisable root access on the SageMaker notebook instances.
- DEnable network isolation for training jobs and models.
- ERestrict notebook presigned URLs to specific IPs used by the company.
- FProtect data with encryption at rest and in transit.
How the community answered
(24 responses)- A75% (18)
- B4% (1)
- C4% (1)
- F17% (4)
Explanation
As per official document only 4 ways to do data egress Enforcing deployment in VPC, Enforcing network isolation, Restricting notebook pre-signed URLs to IPs, Disabling internet access. Read Controlling data egress section Link - https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine- learning-using-amazon-sagemaker/
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