Dell EMC Data Science and BigData Certification Questions and Answers

Question :
In a certain hospital there are two surgeons. Surgeon A operates on 100 patients, and 95 survive.
Surgeon B operates on 80 patients and 72 survive.
We are considering having surgery performed in this hospital and living through the operation is something
that is important. We want to choose the better of the two surgeons.
We did some further research into the data and found that originally the hospital had considered
two different types of surgeries, but then lumped all of the data together to report on each of its surgeons.
Not all surgeries are equal; some were considered high-risk emergency surgeries, while others were
of a more routine nature that had been scheduled in advance.

Of the 100 patients that surgeon A treated, 50 were high risk, of which three died. The other 50
were considered routine, and of these 2 died.
Now we look more carefully at the data for surgeon B and find that of 80 patients, 40 were high risk,
of which seven died. The other 40 were routine and only one died.
Now select the which statement is true about above scenario

1. If your surgery is to be a routine one, then surgeon B is actually the better surgeon
2. If your surgery is to be a routine one, then surgeon A is actually the better surgeon
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4. Data is not sufficient

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Explanation: To make a little more sense of Simpson's paradox, let's look at the following example. In a certain hospital there are two surgeons. Surgeon A operates on 100 patients, and 95 survive. Surgeon B operates on 80
patients and 72 survive. We are considering having surgery performed in this hospital and living through the operation is something that is important. We want to choose the better of the two surgeons.
We look at the data and use it to calculate what percentage of surgeon A's patients survived their operations and compare it to the survival rate of the patients of surgeon B.
95 patients out of 100 survived with surgeon A, so 95/100 = 95% of them survived.
72 patients out of 80 survived with surgeon B, so 72/80 = 90% of them survived.
From this analysis, which surgeon should we choose to treat us? It would seem that surgeon A is the safer bet. But is this really true?
What if we did some further research into the data and found that originally the hospital had considered two different types of surgeries, but then lumped all of the data together to report on each of its surgeons.
Not all surgeries are equal, some were considered high-risk emergency surgeries, while others were of a more routine nature that had been scheduled in advance.
Of the 100 patients that surgeon A treated, 50 were high risk, of which three died. The other 50 were considered routine, and of these 2 died. This means that for a routine surgery, a patient treated by surgeon A has
a 48/50 = 96% survival rate .
Now we look more carefully at the data for surgeon B and find that of 80 patients, 40 were high risk, of which seven died. The other 40 were routine and only one died. This means that a patient has a 39/40 = 97.5%
survival rate for a routine surgery with surgeon A.
Now which surgeon seems better? If your surgery is to be a routine one, then surgeon B is actually the better surgeon. However if we look at all surgeries performed by the surgeons, A is better. This is quite
counterintuitive. In this case the lurking variable of the type of surgery affectsthe combined data of the surgeons.

Question :

You are a doctor in charge of a large hospital, and you have to decide which treatment should be used for a particular disease.
You have the following data from last month: there were 390 patients with the disease. Treatment A was given to 160 patients of
whom 100 were men and 60 were women; 20 of the men and 40 of the women recovered. Treatment B was given to 230 patients of
whom 210 were men and 20 were women; 50 of the men and 15 of the women recovered. Which treatment would you recommend
we use for people with the disease in future?

1. Treatment A, which seemed better in the overall data, was worse for both men and women when considered separately.
2. Treatment B, which seemed better in the overall data, was worse for both men and women when considered separately.
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4. We can safely give everyone treatment B

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Explanation: The simplest way to represent these sort of data is to draw a table, we can then pick the relevant numbers out of the table to calculate the required conditional probabilities. Overall
---------A B
lived 60 65
died 100 165

The probability that a randomly chosen person survived if they were given treatment A is 60/160 = 0.375
The probability that a randomly chosen person survived if they were given treatment B is 65/230 = 0.283
So a randomly chosen person given treatment A was more likely to surive than a randomly chosen person given treatment B. Looks like we'd better give people treatment A.
However, since were given a breakdown of the data by gender, let's look and see if treatment A is better for both genders, or if it gets all of its advantage from one or the other. Women
---------A B
lived 40 15
died 20 5
The probability that a randomly chosen woman survived given that they were given treatment A is 40/60 = 0.67
The probability that a randomly chosen woman survived given that they were given treatment B is 15/20 = 0.75
So it looks like treatment B is better for women. Guess that means treatment A must be much better for men, in order to be better overall. Let's take a closer look.
Men
---------A B
lived 20 50
died 80 160
The probability that a randomly chosen man survived given that they were given treatment A is 20/100 = 0.2
The probability that a randomly chosen man survived given that they were given treatment B is 50/210 = 0.238
So a randomly chosen man was more likely to survive if given treatment B than treatment A. What is going on here?
Treatment A, which seemed better in the overall data, was worse for both men and women when considered separately.
This, in essence, is Simpson's Paradox, partitioning data can result in a reversal of the correlations present in the aggregated data. Why does this happen? Well, essentially for two reasons. Firstly, the treatments
were given to different numbers of people - treatment A was used much less often than treatment B in the example data, and secondly (and probably more importantly) the aggregation is hiding a confounding variable.
Treatment B was much more likely to be given to men than to women, and men are much less likely than women to survive the disease, this obviously makes treatment B look worse in the aggregated data.
So, you might think, we've sorted things out. Gender was the missing variable, and we now know that we can safely give everyone treatment B.

Question :

Select the correct statement for AUC which is a commonly used evaluation method in measuring the accuracy and quality of a recommender system

1. is a commonly used evaluation method for binary choice problems,
2. It involves classifying an instance as either positive or negative
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4. 1 and 2 only
5. All 1,2 and 3
Ans :4
Exp : AUC is a commonly used evaluation method for binary choice problems, which involve classifying an instance as either positive or negative. Its main advantages over other evaluation methods, such as the simpler
misclassification error, are:
1. It's insensitive to unbalanced datasets (datasets that have more installeds than not-installeds or vice versa).
2. For other evaluation methods, a user has to choose a cut-off point above which the target variable is part of the positive class (e.g. a logistic regression model returns any real number between 0 and 1 - the
modeler might decide that predictions greater than 0.5 mean a positive class prediction while a prediction of less than 0.5 mean a negative class prediction). AUC evaluates entries at all cut-off points, giving better
insight into how well the classifier is able to separate the two classes.

Question : You have created a recommender system for QuickTechie.com website, where you recommend the Software professional
based on some parameters like technologies, location, companies etc. Now but you have little doubt that this model is not
giving proper recommendation as Rahul is working on Hadoop in Mumbai and John from france is working on UI application created in flash,
are recommended as a similar professional, which is not correct. Select the correct option which will be helpful to measure the accuracy and quality of a recommender system you created for QuickTechie.com?

1. Cluster Density
2. Support Vector Count
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4. Sum of Absolute Errors

Ans : 3
Exp : AUC is a commonly used evaluation method for binary choice problems, which involve classifying an instance as either positive or negative. Its main advantages over other evaluation methods, such as the simpler
misclassification error, are:
1. It's insensitive to unbalanced datasets (datasets that have more installeds than not-installeds or vice versa).
2. For other evaluation methods, a user has to choose a cut-off point above which the target variable is part of the positive class (e.g. a logistic regression model returns any real number between 0 and 1 - the
modeler might decide that predictions greater than 0.5 mean a positive class prediction while a prediction of less than 0.5 mean a negative class prediction). AUC evaluates entries at all cut-off points, giving better
insight into how well the classifier is able to separate the two classes.

The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references.
Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the
individual differences are weighted equally in the average.

The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.
Support vector count and cluster density do not apply to recommender systems.
MAE and AUC are both valid and useful metrics for measuring recommender systems.

Question :

Scater plots provide the following information about the relationship between two variables
1. Strength
2. Shape - linear, curved, etc.
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4. Presence of outliers

1. 1,2,3
2. 1,3,4
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4. 2,3,4
5. All 1,2,3,4

Ans :5
Exp : Scatter plots show the relationship between two variables by displaying data points on a two-dimensional graph. The variable that might be considered an explanatory variable is plotted on the x axis, and the
response variable is plotted on the y axis.
Scatter plots are especially useful when there are a large number of data points. They provide the following information about the relationship between two variables
Strength
Shape - linear, curved, etc.
Direction - positive or negative
Presence of outliers
A correlation between the variables results in the clustering of data points along a line. The following is an example of a scatter plot suggestive of a positive linear relationship.

Question : You are given a data set that contains information about tv advertisement placed between and of Zee News Channel
(Total Asia continent information). With the following detailed information.
Advertisement duration, Cost rate per minute of Advertissement, Country of the Advertisers, City from which addvertiser
Country to which advertise needs to be shown., City to which advertise needs to be shown., Month total advertisement
Days (of month) advertisement shown, Total hourds for which advertisement shown. , Total Minutes for which advertisement shown.
From the data set you can determine the frequencies of all the advertisement shown in Asia continent. For example, between 1990 and 2014,
500 advertisement were given from China to Shown in India, While 2000 advertisement given by Russia to shown in Japan.
Now you want to draw the pictue which shows the relation between Ad duration and cost per Minute, which technique you feel would be better.

1. Scatter plot
2. Tree map
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4. Box plot
5. Bar chart

Ans : 1
Exp : A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points,
each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. This kind of plot is also called a scatter
chart, scattergram, scatter diagram, or scatter graph.
A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to
understand complex data sets. Another type of heat map, which is often used in business, is sometimes referred to as a tree map. This type of heat map uses rectangles to represent components of a data set. The largest
rectangle represents the dominant logical division of data and smaller rectangles illustrate other sub-divisions within the data set. The color and size of the rectangles on this type of heat map can correspond to two
different values, allowing the viewer to perceive two variables at once. Tree maps are often used for budget proposals, stock market analysis, risk management, project portfolio analysis, market share analysis,
website design and network management. In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines
extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual
points. To visualize correlations between two variables, a scatter plot is typically the best choice. By plotting the data on a scatter plot, you can easily see any trends in the correlation, such as a linear
relationship, a log normal relationship, or a polynomial relationship. A heat map uses three dimensions and so would be a poor choice for this purpose. Box plots, bar charts, and tree maps do not provide the kind of
uniform special mapping of the data onto the graph that is required to see trends.

Question :

Which of the following provide the kind of uniform special mapping of the data onto the graph that is required to see trends.

1. Box plots
2. Bar charts
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4. All 1,2 and 3
5. None of 1,2 and 3

Ans 5
Exp : Box Plots:
In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes
(whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points.
Box plots display differences between populations without making any assumptions of the underlying statistical distribution: they are non-parametric. The spacings between the different parts of the box help indicate
the degree of dispersion (spread) and skewness in the data, and identify outliers. In addition to the points themselves, they allow one to visually estimate various L-estimators, notably the interquartile range,
midhinge, range, mid-range, and trimean. Boxplots can be drawn either horizontally or vertically.
A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to
understand complex data sets.
In the United States, many people are familiar with heat maps from viewing television news programs. During a presidential election, for instance, a geographic heat map with the colors red and blue will quickly inform
the viewer which states each candidate has won.
Another type of heat map, which is often used in business, is sometimes referred to as a tree map. This type of heat map uses rectangles to represent components of a data set. The largest rectangle represents the
dominant logical division of data and smaller rectangles illustrate other sub-divisions within the data set. The color and size of the rectangles on this type of heat map can correspond to two different values,
allowing the viewer to perceive two variables at once. Tree maps are often used for budget proposals, stock market analysis, risk management, project portfolio analysis, market share analysis, website design and
network management.

Question : You are given a data set that contains information about tv advertisement placed between and of Zee News Channel
(Total Asia continent information). With the following detailed information.
Advertisement duration, Cost rate per minute of Advertissement, Country of the Advertisers, City from which addvertiser
Country to which advertise needs to be shown., City to which advertise needs to be shown., Month total advertisement
Days (of month) advertisement shown, Total hourds for which advertisement shown. , Total Minutes for which advertisement shown.
From the data set you can determine the frequencies of all the advertisement shown in Asia continent. For example, between 1990 and 2014,
500 advertisement were given from China to Shown in India, While 2000 advertisement given by Russia to shown in Japan.
Now you want to draw the pictue which shows the relation between which contries given most advertisement in the other country.
Select the correct option.

1. Heat map
2. Tree map
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4. Bar chart
5. Scatter plot

Ans :1 Exp : A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of
points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. This kind of plot is also called a
scatter chart, scattergram, scatter diagram, or scatter graph.
A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to
understand complex data sets.
Another type of heat map, which is often used in business, is sometimes referred to as a tree map. This type of heat map uses rectangles to represent components of a data set. The largest rectangle represents the
dominant logical division of data and smaller rectangles illustrate other sub-divisions within the data set. The color and size of the rectangles on this type of heat map can correspond to two different values,
allowing the viewer to perceive two variables at once. Tree maps are often used for budget proposals, stock market analysis, risk management, project portfolio analysis, market share analysis, website design and
network management. In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically
from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points.
To visualize correlations between two variables, a scatter plot is typically the best choice. By plotting the data on a scatter plot, you can easily see any trends in the correlation, such as a linear relationship, a
log normal relationship, or a polynomial relationship. A heat map uses three dimensions and so would be a poor choice for this purpose. Box plots, bar charts, and tree maps do not provide the kind of uniform special
mapping of the data onto the graph that is required to see trends.In order to effectively visualize the advertisement source and destination frequencies, you'll need a plot that gives at least three dimensions: the
source, destination, and frequency. A heat map provides exactly that. Scatter plots, box plots, tree maps, and bar charts provide at most two dimensions. In theory, you could use a three-dimensional variant of one of
the two dimensions graphs, but three-dimensional graphs are never a good idea. Three-dimensional graphs can only be shown in two dimensions in print and hence cause visual distortions to the data. They can also hide
some data points, and they make it very difficult to compare data points from different parts of the graph.

Question :

Which of the following graph can be best presented in two-dimension

1. Scatter plots
2. Box plots
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4. Bar charts

1. 1,2,3
2. 2,3,4
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4. 1,2,4
5. All 1,2,3 and 4

Ans : 5
Exp : A heat map provides exactly that. Scatter plots, box plots, tree maps, and bar charts provide at most two dimensions. In theory, you could use a three-dimensional variant of one of the two dimensions graphs, but
three-dimensional graphs are never a good idea. Three-dimensional graphs can only be shown in two dimensions in print and hence cause visual distortions to the data. They can also hide some data points, and they make
it very difficult to compare data points from different parts of the graph.

Question : You are given a data set that contains information about tv advertisement placed between and of Zee News Channel
(Total Asia continent information). With the following detailed information.
Advertisement duration, Cost rate per minute of Advertissement, Country of the Advertisers, City from which addvertiser
Country to which advertise needs to be shown., City to which advertise needs to be shown., Month total advertisement
Days (of month) advertisement shown, Total hourds for which advertisement shown. , Total Minutes for which advertisement shown.
From the data set you can determine the frequencies of all the advertisement shown in Asia continent. For example, between 1990 and 2014,
500 advertisement were given from China to Shown in India, While 2000 advertisement given by Russia to shown in Japan.
Now you want to draw the pictue which shows the relation between Ad dthat every city and country has of the overall ad data, which technique you feel would be better.

1. Scatter plot
2. Heat map
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4. Tree map
Ans : 4
Exp : To show the share of advertisement originations for every city and state, you'll need a way to show hierarchical information. A tree map is a natural choice, since it's designed for exactly that purpose. You
could, however, use a stacked bar chart to present the same information. A heat map has an extra, unneeded dimension, which would make the graph confusing. A scatter plot is for numeric data in both dimensions. A box
plot is for groupings of multiple values.
A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data.
The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.
This kind of plot is also called a scatter chart, scattergram, scatter diagram, or scatter graph.
A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to
understand complex data sets.
Another type of heat map, which is often used in business, is sometimes referred to as a tree map. This type of heat map uses rectangles to represent components of a data set. The largest rectangle represents the
dominant logical division of data and smaller rectangles illustrate other sub-divisions within the data set. The color and size of the rectangles on this type of heat map can correspond to two different values,
allowing the viewer to perceive two variables at once. Tree maps are often used for budget proposals, stock market analysis, risk management, project portfolio analysis, market share analysis, website design and
network management.
In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes
(whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points.
To visualize correlations between two variables, a scatter plot is typically the best choice. By plotting the data on a scatter plot, you can easily see any trends in the correlation, such as a linear relationship, a
log normal relationship, or a polynomial relationship. A heat map uses three dimensions and so would be a poor choice for this purpose. Box plots, bar charts, and tree maps do not provide the kind of uniform special
mapping of the data onto the graph that is required to see trends. In order to effectively visualize the advertisement source and destination frequencies, you'll need a plot that gives at least three dimensions: the
source, destination, and frequency. A heat map provides exactly that. Scatter plots, box plots, tree maps, and bar charts provide at most two dimensions. In theory, you could use a three-dimensional variant of one of
the two dimensions graphs, but three-dimensional graphs are never a good idea. Three-dimensional graphs can only be shown in two dimensions in print and hence cause visual distortions to the data. They can also hide
some data points, and they make it very difficult to compare data points from different parts of the graph.

Question :

Which of the following is a correct use case for the scatter plots

1. Male versus female likelihood of having lung cancer at different ages
2. technology early adopters and laggards' purchase patterns of smart phones
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4. All of the above
Ans :4
Exp : Looking to dig a little deeper into some data, but not quite sure how - or if - different
pieces of information relate? Scatter plots are an effective way to give you a sense of
trends, concentrations and outliers that will direct you to where you want to focus your
investigation efforts further.
When to use scatter plots:
o Investigating the relationship between different variables. Examples: Male
versus female likelihood of having lung cancer at different ages, technology early
adopters' and laggards' purchase patterns of smart phones, shipping costs of
different product categories to different regions.

Question :

Which of the following places where we cannot use Gantt charts

1. Displaying a project schedule. Examples: illustrating key deliverables, owners, and deadlines.
2. Showing other things in use over time. Examples: duration of a machine's use,
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4. None of the above
Ans : 4
Exp : Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting
deadlines is paramount to a project's success. Seeing what needs to be accomplished -
and by when - is essential to make this happen. This is where a Gantt chart comes in.
While most associate Gantt charts with project management, they can be used to
understand how other things such as people or machines vary over time. You could
use a Gantt, for example, to do resource planning to see how long it took people to hit
specific milestones, such as a certification level, and how that was distributed over time.
When to use Gantt charts:
o Displaying a project schedule. Examples: illustrating key deliverables, owners,
and deadlines.
o Showing other things in use over time. Examples: duration of a machine's use,
availability of players on a team.

Question :

Which of the following is the best example where we can use Heat maps

1. Segmentation analysis of target market
2. product adoption across regions
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4. All of the above
5. None of 1,2 and 3
Ans : 4
Exp : Heat maps are a great way to compare data across two categories using color. The
Effect is to quickly see where the intersection of the categories is strongest and weakest.
When to use heat maps:
Showing the relationship between two factors. Examples: segmentation analysis of target market, product adoption across regions, sales leads by Individual rep.

Question :

Which of the following cannot be presented using TreeMap?

1. Storage usage across computer machines
2. managing the number and priority of technical support cases
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4. None of the above

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Explanation: Treemap
Looking to see your data at a glance and discover how the different pieces relate
to the whole? Then treemaps are for you. These charts use a series of rectangles,
nested within other rectangles, to show hierarchical data as a proportion to the whole.
As the name of the chart suggests, think of your data as related like a tree: each
branch is given a rectangle which represents how much data it comprises. Each
rectangle is then sub-divided into smaller rectangles, or sub-branches, again based
on its proportion to the whole. Through each rectangle's size and color, you can often
see patterns across parts of your data, such as whether a particular item is relevant,
even across categories. They also make efficient use of space, allowing you to see
your entire data set at once.
When to use treemaps:
o Showing hierarchical data as a proportion of a whole: Examples: storage
usage across computer machines, managing the number and priority of technical
support cases, comparing fiscal budgets between years

Dell EMC Data Science and BigData Certification Questions and Answers

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