Databricks Certified Professional Data Scientist Exam Questions Instant Access

Question 1

Refer to image below

AOption A

BOption B

COption C

DOption D

Answer : A

Question 2

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

AClassification

BClustering

CUnsupervised learning

DNone of the above
Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.

Answer : A

Question 3

Refer to Exhibit

In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan. Which analytical method could produce the probabilities needed to build this exhibit?

ALinear Regression

BLogistic Regression

CDiscriminant Analysis

DAssociation Rules

Answer : B

Question 4

Which of the following statement true with regards to Linear Regression Model?

AOrdinary Least Square can be used to estimates the parameters in linear model

BIn Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.

COrdinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.

DOrdinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
Linear regression model are represented using the below equation

Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1). And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.

Answer : A, D

Question 5

Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:

In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.

Select the correct statement

APrecision is low, which means the classifier is predicting positives best

BPrecision is low, which means the classifier is predicting positives poorly

Cproblem domain has a major impact on the measures that should be used to evaluate a classifier within it

D1 and 3

E2 and 3
In this case, Precision = 50%, Recall = 83%, Specificity = 95%: and Accuracy = 95%. In this case, Precision is low, which means the classifier is predicting positives poorly. However, the three other measures seem to suggest that this is a good classifier. This just goes to show that the problem domain has a major impact on the measures that should be used to evaluate a classifier within it, and that looking at the 4 simple cases presented is not sufficient.

Answer : E

Question 6

In which of the following scenario you should apply the Bay's Theorem

AThe sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.

BWithin the sample space, there exists an event B, for which P(B) > 0.

CThe analytical goal is to compute a conditional probability of the form: P(Ak | B ).

DIn all above cases

Answer : D

Question 7

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

Answer : D