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NEW QUESTION 23
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

A. Cluster raw data using k-means and use sample data from each cluster to build a new datasetB. Run self-correlation on all features and remove highly correlated featuresC. Use an autoencoder or principal component analysis (PCA) to replace original features with new featuresD. Normalize all numerical values to be between 0 and 1

Answer: D

 

NEW QUESTION 24
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

A. Concatenate the features with high correlation scores by using a Jupyter notebook.B. Drop the features with low correlation scores by using a Jupyter notebook.C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.D. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

Answer: C

 

NEW QUESTION 25
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

A. Binomial distributionB. Normal distributionC. Poisson distributionD. Uniform distribution

Answer: A

 

NEW QUESTION 26
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)

A. Apply principal component analysis (PCA).B. Apply min-max feature scaling to the dataset.C. Remove a portion of highly correlated features from the dataset.D. Use a linear-based algorithm to train the model.E. Apply one-hot encoding category-based variables.

Answer: A,B

Explanation:
Reference:
https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html

 

NEW QUESTION 27
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