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Databricks-Certified-Professional-Data-Scientist – Databricks Certified Professional Data Scientist Exam

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Showing 4–6 of 10 questions

Question 4

Select the correct statement which applies to K-Nearest Neighbors

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    No Assumption about the data

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    Computationally expensive

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    Require less memory

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    Works with Numeric Values


Question 5

Suppose you have been given two Random Variables X and Y, whose joint distribution is already known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y. It is the probability distribution of X when the value of Y is not known. So how do you calculate the marginal distribution of X

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    This is typically calculated by summing the joint probability distribution over Y.

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    This is typically calculated by integrating the joint probability distribution over Y

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    This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y

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    This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.


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    '
    For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is


    Text

    Description automatically generated with low confidence where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out. Bivariate marginal and joint probabilities for discrete random variables are often displayed as two-way tables.
    Similarly for continuous random variables, the marginal probability density function can be written as pX(x). This is

    Diagram

    Description automatically generated with medium confidence

    where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the conditional distribution for X given Y Again: the variable Y has been marginalized out.
    Note that a marginal probability can always be written as an expected value:

    Text, letter

    Description automatically generated
    Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e.
    in general


    A picture containing diagram

    Description automatically generated





Question 6

What is the considerable difference between L1 and L2 regularization?

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    L1 regularization has more accuracy of the resulting model

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    Size of the model can be much smaller in L1 regularization than that produced by L2regularization

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    L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.

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    All of the above are correct