AML AML Feature Engineering & Data Preprocessing 1

Question 1

What is the primary purpose of feature scaling in machine learning?

Accepted Answer

To ensure all features contribute equally to model training

Answer

Feature scaling normalizes feature ranges so that no single feature dominates model training due to its magnitude.

Question 2

Which technique handles missing values by replacing them with the average of the column?

Accepted Answer

Mean imputation

Answer

Mean imputation replaces missing values with the column's average, preserving the dataset's overall distribution.

Question 3

What does one-hot encoding do to categorical variables?

Accepted Answer

Creates binary columns for each category

Answer

One-hot encoding converts each category into a separate binary column, avoiding the assumption of ordinal relationships.

Question 4

Which dimensionality reduction technique projects data onto directions of maximum variance?

Accepted Answer

PCA

Answer

PCA (Principal Component Analysis) finds orthogonal axes of maximum variance to compress feature dimensions.

Question 5

What is the primary benefit of feature selection in a machine learning pipeline?

Accepted Answer

Reduces overfitting and training time

Answer

Feature selection removes irrelevant or redundant features, reducing overfitting risk and computational cost.

(AML) Advanced Machine Learning Certification Practice Test