Dell EMC Data Science and BigData Certification Questions and Answers

Question : You have run the association rules algorithm on your data set, and the two rules {banana, apple}
=> {grape} and {apple, orange}=> {grape} have been found to be relevant. What else must be true?

1. {grape, apple, orange} must be a frequent itemset.
2. {banana, apple, grape, orange} must be a frequent itemset.
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4. {banana, apple} => {orange} must be a relevant rule.

similar interests. For example, association rules may suggest that those customers who have bought product A have also bought product B, or those customers who have bought products A, B, and C are more similar to this
customer. These findings provide opportunities for retailers to cross-sell their products. Association rule mining is primarily focused on finding frequent co-occurring associations among a collection of items. It is
sometimes referred to as "Market Basket Analysis", since that was the original application area of association mining. The goal is to find associations of items that occur together more often than you would expect
from a random sampling of all possibilities. The classic example of this is the famous Beer and Diapers association that is often mentioned in data mining books. The story goes like this: men who go to the store to
buy diapers will also tend to buy beer at the same time. Let us illustrate this with a simple example. Suppose that a store's retail transactions database includes the following information:

There are 600,000 transactions in total.
7,500 transactions contain diapers (1.25 percent)
60,000 transactions contain beer (10 percent)
6,000 transactions contain both diapers and beer (1.0 percent)
If there was no association between beer and diapers (i.e., they are statistically independent), then we expect only 10% of diaper purchasers to also buy beer (since 10% of all customers buy beer). However, we
discover that 80% (=6000/7500) of diaper purchasers also buy beer. This is a factor of 8 increase over what was expected - that is called Lift, which is the ratio of the observed frequency of co-occurrence to the
expected frequency. This was determined simply by counting the transactions in the database. So, in this case, the association rule would state that diaper purchasers will also buy beer with a Lift factor of 8. In
statistics, Lift is simply estimated by the ratio of the joint probability of two items x and y, divided by the product of their individual probabilities: Lift = P(x,y)/[P(x)P(y)]. If the two items are statistically
independent, then P(x,y)=P(x)P(y), corresponding to Lift = 1 in that case. Note that anti-correlation yields Lift values less than 1, which is also an interesting discovery - corresponding to mutually exclusive items
that rarely co-occur together.

Question : When would you use a Wilcoxson Rank Sum test?

1. When the data can easily be sorted
2. When the populations represent the sums of other values
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4. When you cannot make an assumption about the distribution of the populations

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Explanation: The Wilcoxon signed-rank test is a non-parametric statistical hypothesis test used when comparing two related samples, matched samples, or repeated measurements on a single sample to assess
whether their population mean ranks differ (i.e. it is a paired difference test). It can be used as an alternative to the paired Student's t-test, t-test for matched pairs, or the t-test for dependent samples when the
population cannot be assumed to be normally distributed. Assumptions : Data are paired and come from the same population. Each pair is chosen randomly and independently. The data are measured at least on an ordinal
scale (cannot be nominal). The Wilcoxon signed-rank test is the nonparametric test equivalent to the dependent t-test. As the Wilcoxon signed-rank test does not assume normality in the data, it can be used when this
assumption has been violated and the use of the dependent t-test is inappropriate. It is used to compare two sets of scores that come from the same participants. This can occur when we wish to investigate any change
in scores from one time point to another, or when individuals are subjected to more than one condition. For example, you could use a Wilcoxon signed-rank test to understand whether there was a difference in smokers'
daily cigarette consumption before and after a 6 week hypnotherapy programme (i.e., your dependent variable would be "daily cigarette consumption", and your two related groups would be the cigarette consumption values
"before" and "after" the hypnotherapy programme). You could also use a Wilcoxon signed-rank test to understand whether there was a difference in reaction times under two different lighting conditions (i.e., your
dependent variable would be "reaction time", measured in milliseconds, and your two related groups would be reaction times in a room using "blue light" versus "red light"). This "quick start" guide shows you how to
carry out a Wilcoxon signed-rank test using SPSS Statistics, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different
assumptions that your data must meet in order for a Wilcoxon signed-rank test to give you a valid result. We discuss these assumptions next. SPSS Statisticstop : Assumptions : When you choose to analyse your data
using a Wilcoxon signed-rank test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a Wilcoxon signed-rank test. You need to do this because it is
only appropriate to use a Wilcoxon signed-rank test if your data "passes" three assumptions that are required for a Wilcoxon signed-rank test to give you a valid result. The first two assumptions relate to your study
design and the types of variables you measured. The third assumption reflects the nature of your data and is the one assumption you test using SPSS Statistics. These three assumptions as briefly explained below:
Assumption #1: Your dependent variable should be measured at the ordinal or continuous level. Examples of ordinal variables include Likert scales (e.g., a 7-point scale from "strongly agree" through to "strongly
disagree"), amongst other ways of ranking categories (e.g., a 5-point scale explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot"). Examples of continuous variables (i.e.,
interval or ratio variables) include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. You can learn more
about ordinal and continuous variables in our article: Types of Variable. Assumption #2: Your independent variable should consist of two categorical, "related groups" or "matched pairs". "Related groups" indicates
that the same subjects are present in both groups. The reason that it is possible to have the same subjects in each group is because each subject has been measured on two occasions on the same dependent variable. For
example, you might have measured 10 individuals' performance in a spelling test (the dependent variable) before and after they underwent a new form of computerized teaching method to improve spelling. You would like
to know if the computer training improved their spelling performance. The first related group consists of the subjects at the beginning (prior to) the computerized spelling training and the second related group
consists of the same subjects, but now at the end of the computerized training. The Wilcoxon signed-rank test can also be used to compare different subjects within a "matched-pairs" study design, but this does not
happen very often. Nonetheless, to learn more about the different study designs you use with a Wilcoxon signed-rank test, see our enhanced Wilcoxon signed-rank test guide. Assumption #3: The distribution of the
differences between the two related groups (i.e., the distribution of differences between the scores of both groups of the independent variable; for example, the reaction time in a room with "blue lighting" and a room
with "red lighting") needs to be symmetrical in shape. If the distribution of differences is symmetrically shaped, you can analyse your study using the Wilcoxon signed-rank test. In practice, checking for this
assumption just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is
not a difficult task. However, do not be surprised if, when analysing your own data using SPSS Statistics, this assumption is violated (i.e., is not met). This is not uncommon when working with real-world data rather
than textbook examples, which often only show you how to carry out a Wilcoxon signed-rank test when everything goes well! However, even when your data fails this assumption, there is often a solution to overcome this,
such as transforming your data to achieve a symmetrically-shaped distribution of differences (not a preferred option) or running a sign test instead of the Wilcoxon signed-rank test. If you are unsure of the
procedures in SPSS Statistics to test this assumption or how to interpret the SPSS Statistics output, we show you how in our enhanced Wilcoxon signed-rank test guide, which you can access by subscribing to the site
here. In the section, Test Procedure in SPSS Statistics, we illustrate the SPSS Statistics procedure to perform a Wilcoxon signed-rank test. First, we introduce the example that is used in this "quick start" guide.
SPSS Statisticstop

Question : In the MapReduce framework, what is the purpose of the Reduce function?

1. It writes the output of the Map function to storage
2. It breaks the input into smaller components and distributes to other nodes in the cluster
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4. It distributes the input to multiple nodes for processing

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Explanation: The reducer

After the mapper, and before the reducer, the shuffler and combining phases take place. The shuffler phase assures that every key value pair with the same key goes to the same reducer, the combining part converts all
the key value pairs of the same key to the grouping form key,list(values), which is what the reducer ultimately receives.

The more standard reducer's job is to take the key list(values) pair, operate on the grouped values, and store it somewhere. That is exactly what our reducer does. It takes the key list(values) pair, loop through the
values concatenating them to a pipe-separated string, and send the new key value pair to the output, so the pair aaa list(aaa,bbb) is converted to aaa aaa|bbb and stored out.

Question : Which of the following is an example of quasi-structured data?

1. OLAP
2. Customer record table
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4. OLTP

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Explanation: Hadoop is adept at processing data that is not arranged in neat rows and tables, such as log-file data and click-stream data. While this data is unstructured compared to highly organized data housed in
relational databases, it does actually possess some limited structure.

For instance, click-stream data is simply a recording of every page request made by a user. It includes some limited structural elements - such as when the request was made and who the user is - that makes it possible
to compare (with technologies like Hadoop) historical click-stream data to identify patterns and predict future user behavior.

This type of data, which includes tags and other identifying markers, would require significant prep work in order to fit into a traditional row-based relational database, but to say it lacks any structure is not
entirely accurate either. Some even call it semi-structured data.

Then there's unstructured content, and there is a difference. Unstructured content, in my opinion, refers to documents, emails, and other objects that are made up of free-flowing text. The contents of a Tweet, for
example, are a type of unstructured content (while Tweet metadata - when it was posted and by who - is unstructured data). Textual data with erratic data formats that can be formatted with effort, tools, and time (for
instance, web clickstream data that may contain
inconsistencies in data values and formats

Unstructured content lacks just about any form or structure we commonly associate with traditional corporate data. The text can be in any language, follow (or not follow) accepted grammatical rules, and/or mix words
and numbers. But the big difference between unstructured data and unstructured content, as I see it, is not what it is but what you do with it.

Dell EMC Data Science and BigData Certification Questions and Answers

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