MS-DS Master of Data science FREE Master of Data Science Questions and Answers 2

Question 1

Which regularization technique adds the absolute value of coefficients as a penalty term to the loss function?

Accepted Answer

L1 (Lasso) regularization

Answer

L1 regularization, also called Lasso, penalizes the sum of absolute values of model coefficients, which can drive some coefficients to exactly zero.

Question 2

In a Master of Data Science program, what does the term 'curse of dimensionality' refer to?

Accepted Answer

Data becomes sparse as the number of features increases

Answer

The curse of dimensionality describes how data points become increasingly sparse as the number of dimensions grows, making distance-based methods less effective.

Question 3

Which evaluation metric is most appropriate for a highly imbalanced binary classification dataset?

Accepted Answer

Area Under the Precision-Recall Curve (AUPRC)

Answer

AUPRC is preferred for imbalanced datasets because it focuses on the performance of the minority class without being inflated by correct majority class predictions.

Question 4

What is the primary purpose of cross-validation in a data science workflow?

Accepted Answer

To estimate how well a model generalizes to unseen data

Answer

Cross-validation partitions data into multiple train-test splits to provide a more reliable estimate of model performance on new, unseen data.

Question 5

Which SQL operation is used to combine rows from two tables based on a related column?

Accepted Answer

JOIN

Answer

JOIN combines rows from two or more tables by matching values in a shared column, which is fundamental to relational database querying in data science.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science FREE Master of Data Science Questions and Answers 2