Data Science Program Quiz 2

Data Science Quiz 2

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1. Which of the following is NOT a dimensionality reduction technique?

K-Nearest Neighbors (KNN)

T-distributed Stochastic Neighbor Embedding (t-SNE)

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

2. What is the main goal of Linear Discriminant Analysis (LDA)?

Optimizing the separation of classes in a dataset

Reducing multicollinearity in features

Maximizing variance in the dataset

Finding principal components with the highest variance

3. Which of the following is a challenge in data collection?

Limited programming languages available

Missing or inconsistent data

Too many structured datasets

Inability to store data

4. What is the most common technique for handling missing numerical values in data cleaning?

Assigning a random number

Using a deep learning model to predict values

Replacing with the median, mean, or mode

Removing all missing data entries

5. Which of the following file formats is NOT commonly used for storing structured datasets?

TXT

JSON

CSV

MP4

6. Feature Engineering involves:

Only reducing the number of features

Removing redundant data entries

Creating new features that improve model performance

Encoding categorical variables

7. Which encoding method is most suitable for categorical variables with no ordinal relationship?

Binary encoding

Label encoding

One-hot encoding

Mean encoding

8. Which of the following statements about Principal Component Analysis (PCA) is true?

PCA reduces dimensionality by finding new orthogonal features

PCA increases the number of features in the dataset

PCA is mainly used for supervised learning tasks

PCA maintains the interpretability of original features

9. What is a major drawback of using one-hot encoding on categorical variables?

It increases dataset dimensionality significantly

It requires manual feature engineering

It does not work for nominal data

It changes numerical variables into categorical

10. Which of the following is NOT a key step in data preprocessing?

Feature scaling

Model evaluation

Data normalization

Handling missing values

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Data Science Program Quiz 2

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