A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

• A.Execute the built-in FindDuplicates Amazon Athena query.
• B.Create a FindMatches machine learning transform in AWS Glue.
• C.Create an AWS Glue crawler to infer duplicate accounts in the source data.
• D.Search for duplicate accounts in the AWS Glue Data Catalog.

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A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

• A.Residual plots
• B.Area under the curve
• C.Confusion matrix
• D.Root Mean Square Error (RMSE)

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A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

• A.Smooth L1 loss
• B.Softmax
• C.Dropout
• D.Rectified linear units (ReLU)

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A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

• A.Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
• B.Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
• C.Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
• D.Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

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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.onfigure 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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