Exit MLS-C01 AWS Certified Machine Learning - Specialty (MLS-C01)
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Q1 Single choice

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

    Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

  • 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

    Concatenate the features with high correlation scores by using a Jupyter notebook.

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