Pass4sure Databricks Databricks-Certified-Professional-Data-Scientist Dumps

Sample Questions

Databricks-Certified-Professional-Data-Scientist Sample Question 1

A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B). 

A. 1/3 
B. 2/3 
C. 1/6 
D. 2/6 

ANSWER : C

Databricks-Certified-Professional-Data-Scientist Sample Question 2

You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?

A. Association Rules 
B. Naive Bayes Classifier 
C. K-means Clustering 
D. Linear Regression 
E. Grouping 

ANSWER : C

Databricks-Certified-Professional-Data-Scientist Sample Question 3

You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?

A. Decrease the number of measures used 
B. Increase the number of clusters 
C. Decrease the number of clusters 
D. Identify additional measures to add to the analysis

ANSWER : C

Databricks-Certified-Professional-Data-Scientist Sample Question 4

Refer to the exhibit.


You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-ofsquares (wss) data as shown in the exhibit. How many customer groups should you specify?

A. 2 
B. 3 
C. 4 
D. 8 

ANSWER : C

Databricks-Certified-Professional-Data-Scientist Sample Question 5

Spam filtering of the emails is an example of

A. Supervised learning
 B. Unsupervised learning 
C. Clustering 
D. 1 and 3 are correct 
E. 2 and 3 are correct 

ANSWER : A

Databricks-Certified-Professional-Data-Scientist Sample Question 6

Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct 

A. It means that your predictions are on average one star off of what people really think 
B. It means that your predictions are on average two star off of what people really think 
C. It means that your predictions are on average three star off of what people really think 
D. It means that your predictions are on average four star off of what people really think 

ANSWER : A

Databricks-Certified-Professional-Data-Scientist Sample Question 7

You are creating a model for the recommending the book at Amazon.com, so which of the following recommender system you will use you don't have cold start problem?

A. Naive Bayes classifier 
B. Item-based collaborative filtering 
C. User-based collaborative filtering
 D. Content-based filtering

ANSWER : D

Databricks-Certified-Professional-Data-Scientist Sample Question 8

Select the correct statement regarding the naive Bayes classification 

A. it only requires a small amount of training data to estimate the parameters 
B. Independent variables can be assumed 
C. only the variances of the variables for each class need to be determined 
D. for each class entire covariance matrix need to be determined

ANSWER : A,B,C

Databricks-Certified-Professional-Data-Scientist Sample Question 9

Classification and regression are examples of___________. 

A. supervised learning 
B. un-supervised learning 
C. Clustering 
D. Density estimation 

ANSWER : A

Databricks-Certified-Professional-Data-Scientist Sample Question 10

Select the correct option from the below

 A. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.
 B. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.
C. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or +_to -_, then you need to look unsupervised learning 
D. If you're not trying to predict a target value, then you need to look into unsupervised learning 
E. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. 

ANSWER : A,B,D,E

Databricks-Certified-Professional-Data-Scientist Sample Question 11

You are working on a Data Science project and during the project you have been gibe a responsibility to interview all the stakeholders in the project. In which phase of the project you are? 

A. Discovery 
B. Data Preparations 
C. Creating Models 
D. Executing Models 
E. Creating visuals from the outcome 
F. Operationnalise the models

ANSWER : A

Databricks-Certified-Professional-Data-Scientist Sample Question 12

What are the key outcomes of the successful analytical projects? 

A. Code of the model 
B. Technical specifications 
C. Presentations for the Analysts 
D. Presentation for Project Sponsors 

ANSWER : A,B,C,D

Databricks-Certified-Professional-Data-Scientist Sample Question 13

Which of the below best describe the Principal component analysis 

A. Dimensionality reduction 
B. Collaborative filtering 
C. Classification 
D. Regression 
E. Clustering 

ANSWER : A

Databricks-Certified-Professional-Data-Scientist Sample Question 14

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 ?

A. Binomial(X+Y,X+Y)
 B. Poisson(X/Y) 
C. Normal(X+Y(M+Y)1/2) 
D. Poisson(X+Y)

ANSWER : D

Databricks-Certified-Professional-Data-Scientist Sample Question 15

Select the sequence of the developing machine learning applications A) Analyze the input data B) Prepare the input data C) Collect data D) Train the algorithm E) Test the algorithm F) Use It

A. A, B, C, D, E, F 
B. C, B, A, D, E, F 
C. C, A, B, D, E, F 
D. C, B, A, D, E, F

ANSWER : D

Databricks-Certified-Professional-Data-Scientist Sample Question 16

Digit recognition, is an example of..... 

A. Classification 
B. Clustering 
C. Unsupervised learning 
D. None of the above

ANSWER : A

Databricks-Certified-Professional-Data-Scientist Sample Question 17

In which of the scenario you can use the linear regression model?

A. Predicting Home Price based on the location and house area 
B. Predicting demand of the goods and services based on the weather 
C. Predicting tumor size reduction based on input as number of radiation treatment 
D. Predicting sales of the text book based on the number of students in state

ANSWER : A,B,C,D

Databricks-Certified-Professional-Data-Scientist Sample Question 18

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

A. The mutual information has a meaningful test for statistical significance. 
B. The mutual information can signal non-linear relationships between the dependent and independent variables. 
C. The mutual information is easier to parallelize. 
D. The mutual information doesn't assume that the variables are normally distributed. 

ANSWER : C

Databricks-Certified-Professional-Data-Scientist Sample Question 19

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

A. This is typically calculated by summing the joint probability distribution over Y. 
B. This is typically calculated by integrating the joint probability distribution over Y 
C. This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y 
D. This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y. 

ANSWER : A,B,C,D

Databricks-Certified-Professional-Data-Scientist Sample Question 20

You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process. 

A. Probability of the customer default on loan repayment 
B. Percentage of the customer loan repayment capability 
C. Percentage of the customer should be given loan or not 
D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable". 

ANSWER : D

Databricks-Certified-Professional-Data-Scientist Sample Question 21

Projecting a multi-dimensional dataset onto which vector has the greatest variance? 

A. first principal component 
B. first eigenvector 
C. not enough information given to answer 
D. second eigenvector 
E. second principal component 

ANSWER : A