Data processing jamb past questions and answers

Data processing jamb past questions and answers 

Question: What is the purpose of feature scaling in the context of gradient-based optimization algorithms, such as those used in neural networks?

a) To handle missing values

b) To prevent overfitting

c) To normalize input features for efficient convergence

d) To remove outliers

Answer: c) To normalize input features for efficient convergence

Question: When is log transformation typically applied during data preprocessing?

a) To handle missing values

b) To normalize data with a skewed distribution

c) To remove redundant features

d) To encode ordinal variables

Answer: b) To normalize data with a skewed distribution

Question: What is the role of a data dictionary in the data preprocessing phase?

a) To handle missing values

b) To provide a detailed description of the dataset's features

c) To reduce dimensionality

d) To encode categorical variables

Answer: b) To provide a detailed description of the dataset's features

Question: How does the application of dropout in neural networks contribute to data preprocessing?

a) By removing outliers

b) By introducing noise to the data

c) By handling missing values

d) By reducing model complexity

Answer: b) By introducing noise to the data

Question: In data preprocessing, what is the purpose of a sanity check?

a) To handle missing values

b) To ensure the quality and reliability of the dataset

c) To increase model complexity

d) To remove outliers

Answer: b) To ensure the quality and reliability of the dataset

Question: What is the role of hashing trick in handling high cardinality categorical variables during data preprocessing?

a) To encode ordinal variables

b) To remove outliers

c) To handle missing values

d) To reduce dimensionality

Answer: d) To reduce dimensionality

Question: Why might a researcher use feature scaling techniques like Min-Max scaling instead of standardization in certain scenarios?

a) To handle missing values

b) To maintain the interpretability of features

c) To prevent overfitting

d) To normalize data into a specific range

Answer: d) To normalize data into a specific range

Question: What is the purpose of t-SNE (t-Distributed Stochastic Neighbor Embedding) in dimensionality reduction during data preprocessing?

a) To handle missing values

b) To reduce dimensionality while preserving local relationships

c) To remove outliers

d) To encode ordinal variables

Answer: b) To reduce dimensionality while preserving local relationships

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

a) Feature extraction involves creating new features, while feature selection involves choosing a subset of existing features

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

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

d) Feature extraction and feature selection are synonymous terms

Answer: a) Feature extraction involves creating new features, while feature selection involves choosing a subset of existing features

Question: In time-series analysis, what is the purpose of rolling window statistics during data preprocessing?

a) To handle missing values

b) To smooth the time-series data

c) To create lag features

d) To achieve stationarity

Answer: c) To create lag features

Question: Why is it important to handle duplicate records in a dataset during data preprocessing?

a) To increase model accuracy

b) To reduce computational complexity

c) To prevent bias in the analysis

d) To remove outliers

Answer: c) To prevent bias in the analysis

Question: What is the purpose of cross-feature analysis during data preprocessing?

a) To handle missing values

b) To identify relationships between different features

c) To normalize data

d) To remove outliers

Answer: b) To identify relationships between different features

Question: Why might feature imputation be necessary in data preprocessing?

a) To remove redundant features

b) To handle missing values

c) To reduce dimensionality

d) To encode ordinal variables

Answer: b) To handle missing values

Question: How does data anonymization contribute to privacy preservation during data preprocessing?

a) By handling missing values

b) By encoding ordinal variables

c) By removing outliers

d) By protecting sensitive information in the dataset

Answer: d) By protecting sensitive information in the dataset

Question: What role does a correlation matrix play in data preprocessing?

a) To remove outliers

b) To handle missing values

c) To identify relationships between numerical features

d) To encode ordinal variables

Answer: c) To identify relationships between numerical features