A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

• A.Linear regression
• B.Classification
• C.Clustering
• D.Reinforcement learning

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A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent How should the Specialist frame this business problem'?

• A.Streaming classification
• B.Binary classification
• C.Regression classification
• D.Multi-category classification

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A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?

• A.Train an anomaly detection model instead of an MLP
• B.Add class weights to the MLP's loss function and then retrain
• C.Gather more data using Amazon Mechanical Turk and then retrain
• D.Train an XGBoost model instead of an MLP

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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.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 low variance.

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This graph shows the training and validation loss against the epochs for a neural network.
The network being trained is as follows:
* Two dense layers, one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

• A.Random initialization of weights with appropriate seed
• B.Early stopping
• C.Increasing the number of epochs
• D.Adding another layer with the 100 neurons

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