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

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A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

• A.The precision of the model is 86%, which is greater than the accuracy of the model.
• B.The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
• C.The precision of the model is 86%, which is less than the accuracy of the model.
• D.The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.

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A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )

• A.Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
• B.Download the AWS SDK for the Spark environment
• C.Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
• D.Install the SageMaker Spark library in the Spark environment.
• E.Use the appropriate estimator from the SageMaker Spark Library to train a model.
• F.Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.

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

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

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