This solution correctly addresses all three requirements of the scenario. It employs server-side encryption with AWS Key Management Service (SSE-KMS) for data at rest in Amazon S3, providing robust, auditable encryption. AWS Identity and Access Management (IAM) roles are the standard and most secure method for granting least-privilege access to SageMaker resources, S3 data, and model artifacts. Amazon CloudWatch is the native AWS service for monitoring. SageMaker automatically publishes model performance metrics (e.g., latency, invocation counts) to CloudWatch, enabling continuous monitoring and alerting. All communication between SageMaker and other AWS services is encrypted in transit using TLS by default.

A. This option is incorrect because AWS CloudTrail monitors API activity and user access, not the operational performance metrics of a machine learning model like accuracy or latency.

B. This is incorrect as Amazon Macie is a data security service for discovering and protecting sensitive data. It does not monitor the performance metrics of a SageMaker model.

C. This option is critically flawed because it completely fails to address the requirement for data encryption at rest, which was a mandatory condition in the scenario.

1. AWS SageMaker Developer Guide, "Protect Data at Rest Using Encryption": This section details how to protect data in S3 buckets used for model artifacts and datasets. It explicitly recommends using AWS KMS keys (SSE-KMS) for encryption. (aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html)

2. AWS SageMaker Developer Guide, "SageMaker Roles": This documentation explains that SageMaker requires an IAM role to access other AWS services, such as reading data from and writing model artifacts to Amazon S3. This is the fundamental mechanism for access control. (aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)

3. AWS SageMaker Developer Guide, "Monitor Amazon SageMaker with Amazon CloudWatch": This page confirms that SageMaker sends metrics for endpoints, training jobs, and other components to CloudWatch. It states, "You can monitor your Amazon SageMaker resources using Amazon CloudWatch, which collects raw data and processes it into readable, near real-time metrics." This directly supports its use for performance monitoring. (aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html)

4. AWS SageMaker Developer Guide, "Protect Data in Transit": This document confirms that "Amazon SageMaker uses Transport Layer Security (TLS) to encrypt communication between your client and SageMaker API endpoints" and between SageMaker and other AWS services, fulfilling the in-transit encryption requirement. (aws.amazon.com/sagemaker/latest/dg/encryption-in-transit.html)