A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

• A.Add event type and event value fields to the interactions dataset in Amazon Personalize.
• B.Use the event tracker in Amazon Personalize to include real-time user interactions.
• C.Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
• D.Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

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A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

• A.Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
• B.Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
• C.Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
• D.Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

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