MS Master of Data Science 3

Question 1

Which ensemble method trains multiple models sequentially, where each model focuses on correcting the errors of the previous one?

Accepted Answer

Boosting

Answer

Boosting trains models sequentially, with each subsequent model assigning higher weight to misclassified instances from previous models.

Question 2

In natural language processing, what does TF-IDF stand for and what problem does it solve?

Accepted Answer

Term Frequency-Inverse Document Frequency; it downweights common words across documents

Answer

TF-IDF weights terms by how often they appear in a document (TF) relative to how rare they are across all documents (IDF), reducing the importance of common stop words.

Question 3

A model achieves 99% accuracy on a dataset where 99% of records belong to class A. What metric would better reveal poor performance on class B?

Accepted Answer

F1-score on the minority class

Answer

The F1-score on the minority class captures both precision and recall for underrepresented classes, exposing poor performance that high overall accuracy masks.

Question 4

What is the curse of dimensionality and its main effect on distance-based algorithms like KNN?

Accepted Answer

In high dimensions, all points become approximately equidistant, making distance metrics meaningless

Answer

As dimensions increase, distances between data points converge, so distance-based algorithms like KNN lose their discriminative power.

Question 5

Which data warehouse schema stores a central fact table surrounded by denormalized dimension tables, optimizing for query performance?

Accepted Answer

Star schema

Answer

The star schema places a fact table at the center with denormalized dimension tables radiating outward, minimizing joins and speeding up analytical queries.

MS - Master of Science Practice Test

MS - Master of Science Practice Test

MS Master of Data Science 3