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Dell EMC Data Science and BigData Certification Questions and Answers



Question : Which word or phrase completes the statement? Business Intelligence is to monitoring trends as
Data Science is to ________ trends.
: Which word or phrase completes the statement? Business Intelligence is to monitoring trends as
1. Predicting
2. Discarding
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4. Optimizing

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Explanation: Data Science is different than the traditional Business Analytics in some key areas. For example, data science

uses predictive and prescriptive analytics to predict what might happen using probabilities and confidence levels, not just report tools to report on what did happen.
Note: when we're dealing with historical data, there is a strong desire and need for the data to be 100% accurate. If you have your financial results wrong for the past quarter, folks are likely to go to jail. However
predicting performance for the next quarter is usually measured in probabilities and confidence levels (e.g., "There is a 95% confidence that our revenues will come in next quarter between $200M to $212M).
is used for dealing with and mitigating the uncertainty in the data. It uses several analytic and visualization techniques to understand where uncertainty may lay in the data, and then uses data transformation
techniques to massage the data into a workable form - not perfect, but again not necessary when dealing with probabilities and not absolutes.
is able to create as-needed data transformations (versus the traditional ETL process) to put the data into a format so that it can be combined with other data sources in search in insights about customers, products
and operations.




Question : Consider a scale that has five () values that range from "not important" to "very important". Which
data classification best describes this data?
: Consider a scale that has five () values that range from
1. Nominal
2. Real
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4. Ordinal


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Explanation: Ordinal Data: The next level higher of data classification than nominal data. Numerical data where number is assigned to represent a qualitative description similar to nominal data. However,
these numbers can be arranged to represent worst to best or vice-versa. Ordinal data is a form of discrete data and should apply nonparametric test to analyze.
ratings provided on a FMEA for Severity, Occurrence, and Detection
DETECTION
1 = detectable every time
5 = detectable about 50% of the time
10 = not detectable at all
(All whole numbers from 1 - 10 represent levels of detection capability that are provided by team, customer, standards, or law)

classifying households as low income, middle-income, and high income
Nominal and ordinal data are from imprecise measurements and are referred to as non metric data, sometime referred to as qualitative data.
Ordinal data is also round when ranking sports teams, ranking the best cities to live, most popular beaches, and survey questionnaires.

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The next higher level of data classification. Numerical data where the data can be arranged in a order and the differences between the values are meaningful but not necessarily a zero point. Interval data can be both
continuous and discrete. Zero degrees Fahrenheit does not mean it is the lowest point on the scale, it is just another point on the scale.
The lowest appropriate level for the mean is interval data.
Parametric AND nonparametric statistical techniques can be used to analyze interval data.
Examples in temperature readings, percentage change in performance of machine, and dollar change in price of oil/gallon.

4. Ratio Data:
Similar to interval data EXCEPT has a defined absolute zero point and is the highest level of data measurement. Ratio data can be both continuous and discrete.
Ratio level data has the highest level of usage and can be analyzed in more ways than the other three types of data.
Interval data and ratio data are considered metric data, also called quantitative data.






Question : Which key role for a successful analytic project can provide business domain expertise with a
deep understanding of the data and key performance indicators?
: Which key role for a successful analytic project can provide business domain expertise with a
1. Business User
2. Project Sponsor
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4. Business Intelligence Analyst
5. None of above

Correct Answer : Get Lastest Questions and Answer :

Explanation: Data Science is different than the traditional Business Analytics in some key areas. For example, data science

uses predictive and prescriptive analytics to predict what might happen using probabilities and confidence levels, not just report tools to report on what did happen.
Note: when we're dealing with historical data, there is a strong desire and need for the data to be 100% accurate. If you have your financial results wrong for the past quarter, folks are likely to go to jail. However
predicting performance for the next quarter is usually measured in probabilities and confidence levels (e.g., "There is a 95% confidence that our revenues will come in next quarter between $200M to $212M).
is used for dealing with and mitigating the uncertainty in the data. It uses several analytic and visualization techniques to understand where uncertainty may lay in the data, and then uses data transformation
techniques to massage the data into a workable form - not perfect, but again not necessary when dealing with probabilities and not absolutes.
is able to create as-needed data transformations (versus the traditional ETL process) to put the data into a format so that it can be combined with other data sources in search in insights about customers, products
and operations.






Related Questions

Question : Refer to the Exhibit.
In the Exhibit, the table shows the values for the
input Boolean attributes "A", "B", and "C". It also
shows the values for the output attribute "class".
Which decision tree is valid for the data?

 : Refer to the Exhibit.
1. Tree A
2. Tree B
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4. Tree D




Question : Refer to the Exhibit.
In the Exhibit, the table shows the values for the
input Boolean attributes "A", "B", and "C". It also
shows the values for the output attribute "class".
Which decision tree is valid for the data?
 : Refer to the Exhibit.
1. Tree A
2. Tree B
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4. Tree D




Question : Refer to the exhibit.
You are assigned to do an end of the year sales analysis of 1, 000 different products,
based on the transaction table. Which column in the end of year report requires the
use of a window function?
 : Refer to the exhibit.
1. Total Sales to Date
2. Daily Sales
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4. Maximum Price




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.