A Data Scientist needs to analyze employment dat
a. The dataset contains approximately 10 million
observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

• A.High-degree polynomial transformation
• B.Numerical value binning
• C.One hot encoding
• D.Cross-validation
• E.Logarithmic transformation

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The displayed graph is from a foresting 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 predicts the seasonality well, but not the trend.
• B.The model does not predict the trend or the seasonality well.
• C.The model predicts the trend well, but not the seasonality.
• D.The model predicts both the trend and the seasonality well.

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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 must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

• A.Convert the records to Apache Parquet format.
• B.Convert the records to JSON format.
• C.Convert the records to GZIP CSV format.
• D.Convert the records to XML format.

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A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?

• A.A scatter plot showing the correlation between maximum tree depth and the objective metric.
• B.A scatter plot showing (he performance of the objective metric over each training iteration
• C.A histogram showing whether the most important input feature is Gaussian.
• D.A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.

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