A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

• A.Decrease regularization.
• B.Increase feature combinations.
• C.Decrease dropout.
• D.Increase dropout.
• E.Increase regularization.
• F.Decrease feature combinations.

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A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

• A.Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
• B.Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
• C.Use an 1AM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
• D.Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

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A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix this issue1?

• A.A longer training time
• B.Using some form of regularization
• C.Using a different optimizer
• D.Making the network larger

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A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

• A.Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
• B.Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
• C.Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
• D.Randomly select 10% of the users. Split off all interaction data from these users for the test set.

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An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

• A.The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
• B.The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
• C.The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
• D.The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

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