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

The questions for Databricks-Certified-Professional-Data-Scientist were last updated On Apr 21, 2025

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Question No. 1

Your company has organized an online campaign for feedback on product quality and you have all the responses for the product reviews, in the response form people have check box as well as text field. Now you know that people who do not fill in or write non-dictionary word in the text field are not considered valid feedback. People who fill in text field with proper English words are considered valid response. Which of the following method you should not use to identify whether the response 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 yeS; nO; no English words , 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

Question No. 2

If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?

AP(E1)/P(E2)

BP(E1+E2)/P(E1)

CP(E2)/P(E1)

DP(E2)/(P(E1+E2)

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Correct Answer: C

Question No. 3

Refer to exhibit

You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1. Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C. The results of the regression are seen in the exhibit. You cannot request additional dat

a. what is a way that you could try to increase the R2 of the model without artificially inflating it?

ACreate clusters based on the data and use them as model inputs

BForce all 15 variables into the model as independent variables

CCreate interaction variables based only on variables A, B, and C

DBreak variables A, B, and C into their own univariate models
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variable) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression^ where multiple correlated dependent variables are predicted, rather than a single scalar variable.) In linear regression data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly: linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X: which is the domain of multivariate analysis.

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Correct Answer: A

Question No. 4

Support vector machines (SVMs) are a set of supervised learning methods used for

ALinear classification

BNon-linear classification

CRegression
In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.

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Correct Answer: A, B, C

Question No. 5

Which of the following skills a data scientists required?

AWeb designing to represent best visuals of its results from algorithm.

BHe should be creative

CShould possess good programming skills

DShould be very good at mathematics and statistic

EHe should possess database administrative skills.
Yes a data scientists should have combination of skills like to solve the complex problem he should be creative as well as able to find new solutions and use of existing data. And solve the problem skills required are programming as currently we see SAS, R: Python, Spark, Java and SPSS even day by day new technologies are coming.
To apply various existing and new algorithm using Machine Learning, or Al it require good mathematics and statistics skills (Where the programmer feels, weaknesses). Another skill required is using visualization techniques like Qlik, Tableau etc

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Correct Answer: B, C, D