Data Science Data Science MCQ 2

Question 1

Which technique is most appropriate for reducing the dimensionality of a dataset while preserving as much variance as possible?

Accepted Answer

Principal Component Analysis

Answer

K-means clustering

Answer

Logistic regression

Answer

Random oversampling

Question 2

In a confusion matrix, what does precision measure?

Accepted Answer

True positives divided by all predicted positives

Answer

True positives divided by all actual positives

Answer

Correct predictions divided by total predictions

Answer

True negatives divided by all negatives

Question 3

Which method helps prevent overfitting by penalizing large model coefficients?

Accepted Answer

L2 (Ridge) regularization

Answer

Increasing the learning rate

Answer

Adding more features

Answer

Removing the validation set

Question 4

What is the primary purpose of cross-validation?

Accepted Answer

To estimate model performance on unseen data more reliably

Answer

To increase the size of the training set

Answer

To remove outliers automatically

Answer

To select the learning rate

Question 5

Which distance metric is most commonly used by default in K-means clustering?

Accepted Answer

Euclidean distance

Answer

Manhattan distance

Answer

Cosine similarity

Answer

Hamming distance

Question 6

A model performs well on training data but poorly on test data. This is a sign of what?

Accepted Answer

Overfitting

Answer

Underfitting

Answer

Class imbalance

Answer

Data leakage from test to train

Question 7

Which of the following is a supervised learning task?

Accepted Answer

Predicting house prices from labeled sales data

Answer

Grouping customers into segments without labels

Answer

Reducing dimensions with t-SNE

Answer

Detecting communities in a graph