Dell EMC Data Science and BigData Certification Questions and Answers

Question : You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team
etc. During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?


1. Data Preparation

2. Discovery

3. Operationalize

4. Model planning

5. Model building


Correct Answer : 5
Explanation: To answer whether the model you are building is robust enough or not, you need to have answer below questions at least
- Model is performing as expected with the test data or not?
- Whatever hypothesis defined in the initial phase is being tested or not?
- Do we need more data?
- Domain experts are convinced or not with the model?
And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.





Question : Which of the following statements are correct with regards to R programming vector?
A. Vector always have character types internally
B. Vector is having one dimension.
C. Vector elements are always with the same data types.
D. Vector (1,2,3,"four",TRUE) internally store this value as "1" "2" "3" "four" "TRUE"


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

Correct Answer : 3
Explanation: Vector does not have any dimension and all the elements in the vector always be the same data types.




Question : Which of the following statement is true with regards to Array and List?



1. Array can have mixed data type values while List cannot.

2. List can have mixed data type values while Array cannot.

3. Both List and Array can have mixed data types.

4. Both List and Array can have only same data types.


Correct Answer : 2
Explanation: List can have mixed data types while Array cannot.

Related Questions

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Question : Which of the below is a correct formula for Lift in Association Rule?




1. A
2. B
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4. D
5. E


Question : Suppose you have transactions where transaction appears as below
{M,E} appear 300 times, {M} appear 500 times, {E} appear 400 times, {B} appear 400 times and {M,B} appears 400.
What you can conclude with that?



1. You can say {M,B} had stronger Association then {M,E}

2. You can say {M,E} had stronger Association then {M,B}

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4. You can say M,E are independent



Question : How do you define the leverage, in case of the Apriori algorithms?



1. Support(X and Y) * Support (X) *Support(Y)

2. Support(X and Y) * Support (X) /Support(Y)

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4. Support (X) /Support(Y)

5. Support(X U Y) ( Support (X) *Support(Y))



Question : A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading
history. Therefore, only the style and subject matter of the current article is available for making
recommendations. All of the magazine's articles are stored in a database in a format suitable for
analytics.
Which method should the data scientist try first?

1. K Means Clustering
2. Naive Bayesian
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4. Association Rules




Question : How are window functions different from regular aggregate functions?

1. Rows retain their separate identities and the window function can access more than the current row.
2. Rows are grouped into an output row and the window function can access more than the current row.
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4. Rows are grouped into an output row and the window function can only access the current row.



Question : Consider these item sets:
(hat, scarf, coat)
(hat, scarf, coat, gloves)
(hat, scarf, gloves)
(hat, gloves)
(scarf, coat, gloves)
What is the confidence of the rule (hat, scarf) -> gloves?


1. 66%
2. 40%
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4. 60%