Data processing WAEC past questions and answers

Data processing WAEC past questions and answers

Question: What is the primary purpose of data preprocessing in machine learning?

a) Enhancing data security

b) Improving model performance

c) Reducing computational complexity

d) Enhancing data storage

Answer: b) Improving model performance

Question: Which step in data preprocessing involves filling in missing values in a dataset?

a) Data transformation

b) Data cleaning

c) Data integration

d) Data reduction

Answer: b) Data cleaning

Question: Why is outlier detection important in data preprocessing?

a) To eliminate redundant features

b) To reduce dimensionality

c) To handle noisy data

d) To speed up model training

Answer: c) To handle noisy data

Question: What is the purpose of normalization in data preprocessing?

a) To convert data into a specific range

b) To remove outliers

c) To handle missing values

d) To encode categorical variables

Answer: a) To convert data into a specific range

Question: Which technique is used for handling categorical variables in data preprocessing?

a) Z-score normalization

b) One-hot encoding

c) Min-Max scaling

d) Log transformation

Answer: b) One-hot encoding

Question: What does the term "feature scaling" refer to in data preprocessing?

a) Removing irrelevant features

b) Transforming categorical variables

c) Scaling numerical features to a standard range

d) Handling missing values

Answer: c) Scaling numerical features to a standard range

Question: In data preprocessing, what is the purpose of data transformation?

a) Handling missing values

b) Changing the format of data

c) Scaling features

d) Converting categorical data to numerical

Answer: b) Changing the format of data

Question: Which technique is used for dimensionality reduction in data preprocessing?

a) One-hot encoding

b) Principal Component Analysis (PCA)

c) Data imputation

d) Feature scaling

Answer: b) Principal Component Analysis (PCA)

Question: What is the primary goal of handling imbalanced datasets in data preprocessing?

a) Increasing model accuracy

b) Reducing model complexity

c) Improving model interpretability

d) Handling unequal class distribution

Answer: a) Increasing model accuracy

Question: Which method is commonly used for handling time-series data in data preprocessing?

a) Feature scaling

b) Data imputation

c) Lag transformation

d) One-hot encoding

Answer: c) Lag transformation

Question: What is the purpose of cross-validation in the context of data preprocessing?

a) Handling missing values

b) Assessing model performance

c) Feature scaling

d) Data transformation

Answer: b) Assessing model performance

Question: Which statistical measure is often used for detecting skewness in data during preprocessing?

a) Mean

b) Median

c) Mode

d) Variance

Answer: b) Median

Question: Why is it important to split a dataset into training and testing sets during data preprocessing?

a) To reduce model complexity

b) To increase training time

c) To assess model generalization

d) To eliminate outliers

Answer: c) To assess model generalization

Question: What is the purpose of data imputation in data preprocessing?

a) Transforming data format

b) Handling missing values

c) Removing outliers

d) Encoding categorical variables

Answer: b) Handling missing values

Question: Which technique is used for smoothing time-series data in data preprocessing?

a) Moving averages

b) Principal Component Analysis (PCA)

c) Z-score normalization

d) Min-Max scaling

Answer: a) Moving averages

Question: In data preprocessing, what is the role of feature engineering?

a) Removing redundant features

b) Creating new informative features

c) Handling missing values

d) Data transformation

Answer: b) Creating new informative features

Question: Why do we perform data reduction in data preprocessing?

a) To increase model complexity

b) To improve model interpretability

c) To handle imbalanced datasets

d) To reduce computational complexity

Answer: d) To reduce computational complexity

Question: What is the purpose of encoding ordinal variables in data preprocessing?

a) To convert data into a specific range

b) To handle missing values

c) To represent order or ranking

d) To remove outliers

Answer: c) To represent order or ranking

Question: How does the feature extraction technique differ from feature selection in data preprocessing?

a) Feature extraction creates new features, while feature selection selects existing features

b) Feature extraction reduces dimensionality, while feature selection increases it

c) Feature extraction is only applicable to numerical data, while feature selection works for all data types

d) Feature extraction and feature selection are synonymous terms

Answer: a) Feature extraction creates new features, while feature selection selects existing features

Question: What is the purpose of data integration in data preprocessing?

a) Handling missing values

b) Combining data from multiple sources

c) Reducing dimensionality

d) Smoothing time-series data

Answer: b) Combining data from multiple sources