APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNING PIPELINE
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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When calling the SageMaker SDK training job, ensure the SSE-KMS is used as a parameter during the creation of the training job.
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Create a Lambda function that is invoked when the training job starts to apply SSE-KMS key to the data before starting the training process.
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Ensure the S3 bucket and data have an SSE-KMS key associated with it, and specify the same SSE-KMS Key ID when you create the SageMaker notebook instance and training job.
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Create a SageMaker notebook instance with an SSE-KMS key associated with it. After loading the S3 data onto the notebook instance, encrypt it using SSE-KMS before feeding it into the training job.
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Detailed explanation-1: -Amazon S3 integrates with AWS Key Management Service (AWS KMS) to provide server-side encryption of Amazon S3 objects. Amazon S3 uses AWS KMS keys to encrypt your Amazon S3 objects. The encryption keys that protect your objects never leave AWS KMS unencrypted.
Detailed explanation-2: -S3 server-side encryption uses one of the strongest block ciphers available, 256-bit Advanced Encryption Standard (AES-256), to encrypt the data.
Detailed explanation-3: -Macie automatically detects a large and growing list of sensitive data types, including personally identifiable information (PII) such as names, addresses, and credit card numbers. It also gives you constant visibility of your data stored in Amazon Simple Storage Service (Amazon S3).