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Question 290 - MLS-C01 discussion

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A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.

Which solution will meet these requirements?

A.

Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.

Answers
A.

Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.

B.

Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.

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B.

Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.

C.

Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the 1AM role.

Answers
C.

Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the 1AM role.

D.

Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.

Answers
D.

Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.

Suggested answer: C

Explanation:

When an Amazon SageMaker notebook instance needs to access encrypted data in Amazon S3, the ML specialist must ensure that both Amazon S3 access permissions and AWS Key Management Service (KMS) decryption permissions are properly configured. The dataset in this scenario is stored with server-side encryption using an AWS KMS key (SSE-KMS), so the following steps are necessary:

S3 Read Permissions: Attach an IAM role to the SageMaker notebook instance with permissions that allow the s3:GetObject action for the specific S3 bucket storing the data. This will allow the notebook instance to read data from Amazon S3.

KMS Key Policy Permissions: Grant permissions in the KMS key policy to the IAM role assigned to the SageMaker notebook instance. This allows SageMaker to use the KMS key to decrypt data in the S3 bucket.

These steps ensure the SageMaker notebook instance can access the encrypted data stored in S3. The AWS documentation emphasizes that to access SSE-KMS encrypted data, the SageMaker notebook requires appropriate permissions in both the S3 bucket policy and the KMS key policy, making Option C the correct and secure approach.

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asked 31/10/2024
Matthew Cole
36 questions
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