Data Science Data Science MCQ 5

Question 1

Which evaluation metric is most appropriate for a regression problem?

Accepted Answer

Root Mean Squared Error

Answer

Accuracy

Answer

Precision

Answer

F1 score

Question 2

What is the curse of dimensionality?

Accepted Answer

Data becomes sparse and distances less meaningful as features increase

Answer

Models train faster with more features

Answer

Adding features always improves accuracy

Answer

Categorical features cannot be encoded

Question 3

Which type of join returns only the rows that have matching keys in both tables?

Accepted Answer

INNER JOIN

Answer

LEFT JOIN

Answer

FULL OUTER JOIN

Answer

CROSS JOIN

Question 4

What does a high variance inflation factor (VIF) for a feature indicate?

Accepted Answer

Strong multicollinearity with other features

Answer

The feature is unimportant

Answer

Missing values in the feature

Answer

A non-linear relationship with the target

Question 5

Which technique randomly deactivates neurons during training to reduce overfitting in neural networks?

Accepted Answer

Dropout

Answer

Gradient clipping

Answer

Early stopping

Answer

Weight initialization

Question 6

What is the main goal of feature engineering?

Accepted Answer

Creating informative input variables to improve model performance

Answer

Reducing the number of training epochs

Answer

Selecting the optimizer

Answer

Increasing the test set size

Question 7

Which sampling method ensures each subgroup of a population is proportionally represented?

Accepted Answer

Stratified sampling

Answer

Simple random sampling

Answer

Convenience sampling

Answer

Bootstrap sampling