MS-DS Master of Data science Master of Data Science 2

Question 1

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

Accepted Answer

Lasso (L1)

Answer

Lasso (L1) regularization penalizes the sum of absolute values of coefficients, which can drive some coefficients exactly to zero, performing feature selection.

Question 2

In the context of Bayesian inference, what does the prior distribution represent?

Accepted Answer

Beliefs about parameters before observing data

Answer

The prior distribution encodes beliefs or knowledge about model parameters before any data is observed.

Question 3

Which SQL window function returns the rank of a row within a partition, with no gaps in ranking values?

Accepted Answer

DENSE_RANK()

Answer

DENSE_RANK() assigns consecutive ranks without gaps, whereas RANK() skips values when ties occur.

Question 4

What is the time complexity of training a k-Nearest Neighbors classifier on n training samples with d features?

Accepted Answer

O(1) training, O(nd) at prediction

Answer

kNN is a lazy learner — training is O(1) since it just stores data, but each prediction requires computing distances to all n points across d features.

Question 5

In a neural network, what problem occurs when gradients become exponentially small as they propagate backward through many layers?

Accepted Answer

Vanishing gradient

Answer

The vanishing gradient problem causes gradients to shrink exponentially during backpropagation, making it difficult to train deep networks.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Master of Data Science 2