Most Recent Databricks-Certified-Professional-Data-Scientist Exam Dumps

Prepare for the Databricks Certified Professional Data Scientist Exam exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Databricks-Certified-Professional-Data-Scientist exam and achieve success.

The questions for Databricks-Certified-Professional-Data-Scientist were last updated on Apr 1, 2025.

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Question No. 1

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

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Correct Answer: B

Question No. 2

What are the advantages of the mutual information over the Pearson correlation for text classification problems?

AThe mutual information has a meaningful test for statistical significance.

BThe mutual information can signal non-linear relationships between the dependent and independent variables.

CThe mutual information is easier to parallelize.

DThe mutual information doesn't assume that the variables are normally distributed.
A linear scaling of the input variables (that may be caused by a change of units for the measurements) is sufficient to modify the PCA results. Feature selection methods that are sufficient for simple distributions of the patterns belonging to different classes can fail in classification tasks with complex decision boundaries. In addition, methods based on a linear dependence (like the correlation) cannot take care of arbitrary relations between the pattern coordinates and the different classes. On the contrary, the mutual information can measure arbitrary relations between variables and it does not depend on transformations acting on the different variables.
This item concerns itself with feature selection for a text classification problem and references mutual information criteria. Mutual information is a bit more sophisticated than just selecting based on the simple correlation of two numbers because it can detect non-linear relationships that will not be identified by the correlation. Whenever possible: mutual information is a better feature selection technique than correlation.
Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence.
Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much 'knowledge' one can gain of a certain variable by knowing the value of another variable.

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Correct Answer: C

Question No. 3

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

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

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

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

DThis is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.
Given two random variables X and Y whose joint distribution is 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. This is typically calculated by summing or integrating the joint probability distribution over Y. '
For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is

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

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:

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

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Correct Answer: A, B, C, D

Question No. 4

Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

ANumerical

BCategorical

CBoth 1 and 2 are correct

DNone of the 1 and 2 are correct
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.

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Correct Answer: C

Question No. 5

You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare to the honest claims. Which of the following technique is suitable to find out whether the claim is valid or not?

ANaive Bayes

BLogistic Regression

CRandom Decision Forests

DAny one of the above
In this problem you have been given high-dimensional independent variables like texts, corrections, test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
Support vector machines Naive Bayes Logistic regression Random decision forests

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Correct Answer: D

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