MS Master of Data Science 2

Question 1

Which regularization technique adds the absolute value of coefficients as a penalty term, effectively performing feature selection by shrinking some coefficients to zero?

Accepted Answer

Lasso (L1)

Answer

Lasso (L1) regularization uses the absolute value of coefficients as the penalty, which drives some coefficients to exactly zero, performing automatic feature selection.

Question 2

In the context of A/B testing, what does statistical power represent?

Accepted Answer

The probability of correctly detecting a true effect

Answer

Statistical power (1 - β) is the probability of correctly rejecting a false null hypothesis, i.e., detecting a real effect when one exists.

Question 3

A data scientist needs to join two large Spark DataFrames where one is small enough to fit in memory. Which join strategy minimizes shuffle overhead?

Accepted Answer

Broadcast join

Answer

A broadcast join sends the smaller DataFrame to all worker nodes, eliminating the expensive shuffle operation required by sort-merge joins.

Question 4

Which theorem states that a feedforward neural network with a single hidden layer can approximate any continuous function to arbitrary precision given enough neurons?

Accepted Answer

Universal approximation theorem

Answer

The universal approximation theorem guarantees that a single hidden layer network with sufficient neurons can approximate any continuous function on a compact subset.

Question 5

In SQL window functions, what is the difference between RANK() and DENSE_RANK()?

Accepted Answer

RANK() skips numbers after ties; DENSE_RANK() does not skip numbers

Answer

RANK() leaves gaps after tied values (e.g., 1,1,3), while DENSE_RANK() assigns consecutive ranks without gaps (e.g., 1,1,2).

MS - Master of Science Practice Test

MS - Master of Science Practice Test

MS Master of Data Science 2