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Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Actual Questions

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Question No. 1

Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B. Of those smokers who prefer brand

A30% are females, and of those who prefer brand B. 40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?
Which of the following is a best way to solve this problem?

ABays Theorem

BPoisson Distribution

CBinomial Distribution

DNone of the above

Show Answer

Correct Answer: A, A

Question No. 2

Google Adwords studies the number of men, and women, clicking the advertisement on search

engine during the midnight for an hour each day.

Google find that the number of men that click can be modeled as a random variable with distribution

Poisson(X), and likewise the number of women that click as Poisson(Y).

What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour ?

ABinomial(X+Y,X+Y)

BPoisson(X/Y)

CNormal(X+Y(M+Y)1/2)

DPoisson(X+Y)
The total number of clicks is the sum of the number of men and
women. The sum of two Poisson random variables also follows a Poisson distribution with
rate equal to the sum of their rates.
The Normal and Binomial distribution can approximate the Poisson distribution in
certain cases, but the expressions above do not approximate Poisson(X+Y).

Show Answer

Correct Answer: D

Question No. 3

RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a______, as it is scale-dependent.

ABetween Variables

BParticular Variable

CAmong all the variables

DAll of the above are correct

Show Answer

Correct Answer: B

Question No. 4

What is the considerable difference between L1 and L2 regularization?

AL1 regularization has more accuracy of the resulting model

BSize of the model can be much smaller in L1 regularization than that produced by L2-regularization

CL2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.

DAll of the above are correct
The two most common regularization methods are called L1 and L2 regularization. L1 regularization penalizes the weight vector for its L1-norm (i.e. the sum of the absolute values of the weights), whereas L2 regularization uses its L2-norm. There is usually not a considerable difference between the two methods in terms of the accuracy of the resulting model (Gao et al 2007), but L1 regularization has a significant advantage in practice. Because many of the weights of the features become zero as a result of L1-regularized training, the size of the model can be much smaller than that produced by L2-regularization. Compact models require less space on memory and storage, and enable the application to start up quickly. These merits can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
Regularization works by adding the penalty associated with the coefficient values to the error of the hypothesis. This way, an accurate hypothesis with unlikely coefficients would be penalized whila a somewhat less accurate but more conservative hypothesis with low coefficients would not be penalized as much.
81

Show Answer

Correct Answer: B

Question No. 5

You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.

ACustomer fit as a good

BCustomer fit as acceptable or average category

Cexpressed as a percent, that the customer will default on a loan

D1 and 3 are correct

E2 and 3 are correct
Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.

Show Answer

Correct Answer: C