A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

• A.Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
• B.Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
• C.Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
• D.Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

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For the given confusion matrix, what is the recall and precision of the model?

• A.Recall = 0.84 Precision = 0.8
• B.Recall = 0.92 Precision = 0.8
• C.Recall = 0.92 Precision = 0.84
• D.Recall = 0.8 Precision = 0.92

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A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

• A.Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
• B.Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
• C.Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail.
  Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
• D.Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting

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Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

• A.Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
• B.Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
• C.Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
• D.Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

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An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented Al (Amazon A2I).
Which solution will meet these requirements?

• A.Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.
• B.Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.
• C.Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.
• D.Use AWS Panorama for automatic processing Use Amazon A2I with Amazon Mechanical Turk for manual review

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