A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST accuracy?

• A.Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)
• B.Logistic regression
• C.Support vector machine (SVM) with non-linear kernel
• D.Single perceptron with tanh activation function

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A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

• A.Amazon Comprehend syntax analysts and entity detection
• B.Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
• C.Natural Language Toolkit (NLTK) stemming and stop word removal
• D.Amazon SageMaker BlazingText allow mode

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A company wants to predict the sale prices of houses based on available historical sales dat a. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

• A.Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
• B.Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
• C.Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
• D.Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

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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 long 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. Set up Amazon SNS to receive a notification when the model is overfitting.
• C.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.
• D.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.

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The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A.The model does not predict the trend or the seasonality well.
• B.The model predicts both the trend and the seasonality well
• C.The model predicts the trend well, but not the seasonality.
• D.The model predicts the seasonality well, but not the trend.

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