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 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.
• B.Use AWS KMS to encrypt the data on Amazon S3
• C.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.
• D.Use an IAM 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.

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

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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.Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
• B.Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
• C.Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
• 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 Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.
Which model describes the underlying data in this situation?

• A.A naive Bayesian model, since the features are all conditionally independent.
• B.A full Bayesian network, since the features are all conditionally independent.
• C.A naive Bayesian model, since some of the features are statistically dependent.
• D.A full Bayesian network, since some of the features are statistically dependent.

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A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

• A.Logistic regression
• B.Linear regression
• C.K-means
• D.Principal component analysis (PCA)

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