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

To reduce the number of features

Answer

To remove outliers from the dataset

Answer

To encode categorical variables

Question 2

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

Accepted Answer

Mean imputation

Answer

One-hot encoding

Answer

Label encoding

Answer

Standardization

Question 3

What does one-hot encoding do to categorical variables?

Accepted Answer

Creates binary columns for each category

Answer

Assigns numeric labels to categories

Answer

Removes low-frequency categories

Answer

Scales categories to a 0-1 range

Question 4

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

Accepted Answer

PCA

Answer

t-SNE

Answer

LDA

Answer

UMAP

Question 5

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

Accepted Answer

Reduces overfitting and training time

Answer

Increases model complexity

Answer

Adds synthetic training data

Answer

Improves data imputation accuracy

Question 6

Which outlier detection method uses the interquartile range (IQR) to flag extreme values?

Accepted Answer

Tukey's fence method

Answer

Z-score normalization

Answer

Min-max scaling

Answer

Winsorization