Dell EMC Data Science and BigData Certification Questions and Answers

Question : Which of the following statement is true for the Apriori algorithm?

1. Using the confidence you can say that Rules are Trustworthy and not coincidental

2. Using the confidence you can say that Rules are Trustworthy but not sure whether Rules are coincidental or not.

3. Using the Lift and Leverage you can make sure that rules are identified and filter out the coincidental rules.

4. 1,2
5. 2,3


Correct Answer : 5
Explanation: By applying the confidence you can say that Association rules are trustworthy. However, it is not possible to check whether these rules are coincidental. Hence to filter out the coincidental
rules we can use the Lift and Leverage measures.




Question : In which of the following case you can use the Association Rules?
A. You can manage your inventory using this.
B. You can do cross merchandising like products with the high margin
C. You can logically group all the related products on the portal
D. You can physically keep all the related products together
E. You can run the promotions by combining the products


1. A,B,C
2. B,C,D
3. C,D,E
4. B,C,D,E
5. A,B,C,D,E

Correct Answer : 5
Explanation: As you get know which all products are related to each other using the Association Rule. Hence, you can group combine all the related products together and give discount on them. Even to
manage your inventory you can use this, and even decide whether we should keep all the products together or not.
Even you can use it to create Recommender system or Clickstream analysis.





Question : You have transactions in your dataset. Your marketing team decide that minimum support level is .. How many minimum transactions should be there for an item or combination of item to become
frequent dataset?



1. 30

2. 3000

3. 300

4. 100

5. 3


Correct Answer : 2
Explanation: Minimum support defines that in how many transaction item should be present. So that it can be considered as frequent itemset. So you can calculate as total transaction count * minimum support
level = 10000 * 0.3 = 3000

Related Questions

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Question : Which technique you would be using to solve the below problem statement?
"What is the probability that individual customer will not repay the loan amount?"



1. Classification

2. Clustering

3. Linear Regression

4. Logistic Regression

5. Hypothesis testing



Question : What type of output generated in case of linear regression?



1. Continuous variable

2. Discrete Variable

3. Any of the Continuous and Discrete variable

4. Values between 0 and 1



Question : 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


1. A,B
2. B,C
3. C,D
4. A,B,C
5. A,B,C,D


Question : Which of the following statement true with regards to Linear Regression Model?
A. Ordinary Least Square can be used to estimates the parameters in linear model
B. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
C. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
D. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.


1. A,B
2. B,C
3. C,D
4. A,D
5. B,D


Question : Which of the following is correct definition of Residual?



1. Residual is a mean squared error

2. Residual is calculated as square of actual value minus predicted value

3. Residual is calculated as Actual Value minus Predicted Values

4. Residual is calculated as Actual Value plus Predicted Values



Question : Which of the following statement is true with regards to R square?
A. It helps in finding that how close the data are to the fitted model.
B. R Square has value between 0 to 999
C. R square 0 means the model explains none of the variability of the response data around the mean.
D. R square 999 indicates that the model explains all the variability of the response data around mean.
E. Higher the squared, the better the model fits your data.


1. A,B
2. B,C
3. A,C
4. D,E
5. A,E