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Dell EMC Data Science and BigData Certification Questions and Answers



Question : Select the correct statement which applies to logistic regression

: Select the correct statement which applies to logistic regression
1. Computationally inexpensive, easy to implement, knowledge representation easy to interpret
2. May have low accuracy
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4. Only 1 and 3 are correct
5. All 1,2 and 3 are correct



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Depending on the size of the data you are uploading, Amazon S3 offers the following options:

Logistic regression
Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret
Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values






Question : Suppose that we are interested in the factors that influence whether a political candidate wins an election.
The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of
money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.

Above is an example of


: Suppose that we are interested in the factors that influence whether a political candidate wins an election.
1. Linear Regression
2. Logistic Regression
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4. Maximum likelihood estimation
5. Hierarchical linear models


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Question : A researcher is interested in how variables, such as GRE (Graduate Record Exam scores),
GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school.
The response variable, admit/don't admit, is a binary variable.

Above is an example of


: A researcher is interested in how variables, such as GRE (Graduate Record Exam scores),
1. Linear Regression
2. Logistic Regression
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4. Maximum likelihood estimation
5. Hierarchical linear models


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Related Questions

Question : Refer to the Exhibit.
You are working on creating an OLAP query that outputs several rows of with summary rows of
subtotals and grand totals in addition to regular rows that may contain NULL as shown in the
exhibit. Which function can you use in your query to distinguish the row from a regular row to a
subtotal row?

 : Refer to the Exhibit.
1. GROUPING
2. RANK
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4. ROLLUP




Question : Refer to the exhibit.
After analyzing a dataset, you report findings to your team:
1. Variables A and C are significantly and positively impacting the dependent variable.
2. Variable B is significantly and negatively impacting the dependent variable.
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After seeing your findings, the majority of your team agreed that variable B should be positively
impacting the dependent variable.
What is a possible reason the coefficient for variable B was negative and not positive?

: Refer to the exhibit.
1. The information gain from variable B is already provided by another variable
2. Variable B needs a quadratic transformation due to its relationship to the dependent variable
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4. Variable B needs a logarithmic transformation due to its relationship to the dependent variable




Question : Refer to the exhibit.
You have run a linear regression model against your data, and have plotted true outcome versus
predicted outcome. The R-squared of your model is 0.75. What is your assessment of the model?
 : Refer to the exhibit.
1. The R-squared may be biased upwards by the extreme-valued outcomes. Remove them and
refit to get a better idea of the model's quality over typical data.
2. The R-squared is good. The model should perform well.
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see if the R-squared improves over typical data.
4. The observations seem to come from two different populations, but this model fits them both
equally well.




Question : 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-of-squares (wss)
data as shown in the exhibit. How many customer groups should you specify?
 : Refer to the exhibit.
1. 2
2. 3
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4. 8




Question : Refer to the exhibit.
Click on the calculator icon in the upper left corner. You are given a list of pre-defined association
rules:
A) RENTER => BAD CREDIT
B) RENTER => GOOD CREDIT
C) HOME OWNER => BAD CREDIT
D) HOME OWNER => GOOD CREDIT
E) FREE HOUSING => BAD CREDIT
F) FREE HOUSING => GOOD CREDIT
For your next analysis, you must limit your dataset based on rules with confidence greater than
60%.
Which of the rules will be kept in the analysis?

 : Refer to the exhibit.
1. Rules B and D
2. Rules A and F
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4. Rules D and E




Question : Refer to the exhibit.
You are using k-means clustering to discover groupings within a data set. You plot within-sum-ofsquares
(wss) of multiple cluster sizes. Based on the exhibit, how many clusters should you use in
your analysis?
 : Refer to the exhibit.
1. 2
2. 8
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4. 10