Data processing NECO past questions and answers

Data processing NECO past questions and answers

Question: Which technique is used to address the curse of dimensionality in data preprocessing?

a) Feature scaling

b) Principal Component Analysis (PCA)

c) Data imputation

d) One-hot encoding

Answer: b) Principal Component Analysis (PCA)

Question: What is the purpose of stratified sampling in the context of data preprocessing?

a) Balancing class distribution in training and testing sets

b) Removing outliers from the dataset

c) Handling missing values

d) Feature scaling

Answer: a) Balancing class distribution in training and testing sets

Question: When might data normalization be more appropriate than standardization in data preprocessing?

a) When the data has a Gaussian distribution

b) When the data contains outliers

c) When handling categorical variables

d) When feature scaling is not required

Answer: a) When the data has a Gaussian distribution

Question: In time-series analysis, what does differencing aim to achieve during data preprocessing?

a) Handling missing values

b) Reducing noise in the data

c) Removing outliers

d) Achieving stationarity

Answer: d) Achieving stationarity

Question: What is the primary purpose of oversampling in handling imbalanced datasets during data preprocessing?

a) Reducing model complexity

b) Handling missing values

c) Increasing the representation of minority class

d) Removing redundant features

Answer: c) Increasing the representation of minority class

Question: When performing cross-validation, what is the purpose of the validation set in addition to the training set?

a) To train the model

b) To assess model generalization

c) To increase model complexity

d) To handle missing values

Answer: b) To assess model generalization

Question: How does the technique of data augmentation contribute to data preprocessing in the context of image classification?

a) By reducing dimensionality

b) By introducing noise to the data

c) By creating variations of the existing data

d) By handling missing values

Answer: c) By creating variations of the existing data

Question: What is the role of binning or discretization in data preprocessing?

a) Removing redundant features

b) Handling missing values

c) Converting numerical data into categorical bins

d) Encoding ordinal variables

Answer: c) Converting numerical data into categorical bins

Question: Why is it essential to handle multicollinearity during data preprocessing?

a) To increase model accuracy

b) To reduce feature dimensionality

c) To improve model interpretability

d) To avoid redundancy among features

Answer: d) To avoid redundancy among features

Question: What is the purpose of batch normalization in deep learning models during data preprocessing?

a) To handle missing values

b) To normalize input features within each mini-batch

c) To remove outliers

d) To perform data imputation

Answer: b) To normalize input features within each mini-batch

Question: When is the use of k-fold cross-validation more beneficial than a simple train-test split during data preprocessing?

a) In cases of small datasets

b) In cases of large datasets

c) In cases of imbalanced datasets

d) In cases of missing values

Answer: a) In cases of small datasets

Question: What is the primary purpose of tokenization in natural language processing (NLP) data preprocessing?

a) To handle missing values

b) To convert text data into numerical format

c) To remove outliers

d) To encode ordinal variables

Answer: b) To convert text data into numerical format

Question: Why is it important to handle skewness in numerical features during data preprocessing?

a) To handle missing values

b) To improve model interpretability

c) To achieve a more symmetric distribution

d) To perform data imputation

Answer: c) To achieve a more symmetric distribution

Question: In data preprocessing, what is the purpose of the "train-validation-test" split when developing machine learning models?

a) To assess model generalization

b) To handle missing values

c) To increase model complexity

d) To remove outliers

Answer: a) To assess model generalization

Question: How does the technique of hashing play a role in handling high-dimensional categorical data during data preprocessing?

a) By encoding ordinal variables

b) By creating new informative features

c) By reducing dimensionality

d) By handling missing values

Answer: c) By reducing dimensionality