AWS Certified Machine Learning – Specialty — Question 56

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression.
During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable.
What should be done to reduce the impact of having such a large number of features?

Answer options

• A. Perform one-hot encoding on highly correlated features.
• B. Use matrix multiplication on highly correlated features.
• C. Create a new feature space using principal component analysis (PCA)
• D. Apply the Pearson correlation coefficient.

Correct answer: C

Explanation

The correct answer is C, as principal component analysis (PCA) helps to transform correlated features into a set of values of uncorrelated variables, thereby reducing dimensionality and improving model stability. Options A and B do not address the correlation issue effectively, while D merely measures correlation without providing a solution to the instability problem.