Dell EMC Data Science and BigData Certification Questions and Answers
Question : Which functionality do regular expressions provide?
1. increased numerical precision
2. underflow prevention
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4. decreased processing complexity
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Explanation: A regular expression is a method used in programming for pattern matching. Regular expressions provide a flexible and concise means to match strings of text. For example, a regular expression
could be used to search through large volumes of text and change all occurrences of "cat" to "dog".
Regular expressions are used for syntax highlighting systems, data validation and in search engines such as Google, to try to determine an algorithmic match to the query a user is asking.
Regular expressions are also known in short form as regex or regexp. Utilities, text editors and programming languages use regular expressions to manipulate and search patterns of text. While some languages integrate
regular expressions into the core of the language syntax, like TCL, Awk, PERL and RUBY, others use regular expressions through libraries, such as Java, C++ and C. This means there are implementation differences so a
regular expression that works well with one application might or might not work with another. Subtle differences do exist.
Regular expressions can be incredibly powerful. Essentially, if the pattern can be defined, a regular expression can be created. A simple pattern might be something as simple as finding all situations where a sentence
ends in "that" and is replaced with "which". The pattern could get more complex by doing the same replacement but only on the 3rd and 5th occurrence of a match. Or it could get even more complicated by using different
sets of matching characters depending on the frequency and location of previous matching characters.
The three main components of a regular expression are anchors that are used to specify the position of a pattern in relation to a line of text, character sets that match one or more characters in a single position,
and modifiers that specify the number of times the previous character set is repeated.
The operations that help in building regular expressions are:
Quantification: Quantifiers dictate how often the preceding element is allowed to occur.
Grouping: Operators can have their scope and precedence specified using parentheses.
Boolean Conditions: An OR or AND condition can be stated for operators and groups.
Regular expressions use algorithms such as Deterministic Finite Automation (DFA) and Non-deterministic Finite Automation (NFA) to match a string. In an NFA, for each pair of state and input symbol there are several
possible next states, while a DFA accepts a finite string of symbols.
Question : When creating a project sponsor presentation, what is the main objective?
1. Show that you met the project goals
2. Show how you met the project goals
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4. Clearly describe the methods and techniques used
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Explanation:
Goals are high-level statements that provide the overall context for what the project is trying to accomplish. Let's look at an example and some of the characteristics of a goal statement. One of the goals of a
project might be to "increase the overall satisfaction levels for clients calling to the company helpdesk with support needs". Key software project goals include
Functionality. This is the number-one driver in most software projects, which are obsessed with meeting functional requirements. And for good reason too. Software is a tool meant to solve a problem. Solving the
problem invariably involves getting something done in a particular way.
Usability. People like programs to be easy to use, especially when it comes to conducting electronic commerce. Usability is very important, because without it software becomes too much of a hassle to work with.
Usability affects reliability too, because human error often leads to software failure. The problem is, security mechanisms, including most uses of cryptography, elaborate login procedures, tedious audit requirements,
and so on, often cause usability to plummet. Security concerns regularly impact convenience too. Security people often deride cookies, but cookies are a great user-friendly programming tool!
Efficiency. People tend to want to squeeze every ounce out of their software (even when efficiency isn't needed). Efficiency can be an important goal, although it usually trades off against simplicity. Security often
comes with significant overhead. Waiting around while a remote server somewhere authenticates you is no fun, but it can be necessary.
Time-to-market. "Internet time" happens. Sometimes the very survival of an enterprise requires getting mind share fast in a quickly evolving area. Unfortunately, the first thing to go in a software project with severe
time constraints is any attention to software risk management. Design, analysis, and testing corners are cut with regularity. This often introduces grave security risks. Fortunately, building secure software does not
have to be slow. Given the right level of expertise, a secure system can sometimes be designed more quickly than an ad hoc cousin system with little or no security.
Simplicity. The good thing about simplicity is that it is a good idea for both software projects and security. Everyone agrees that keeping it simple is good advice.
Question : The average purchase size from your online sales site is $, . The customer experience team
believes a certain adjustment of the website will increase sales. A pilot study on a few hundred
customers showed an increase in average purchase size of $1.47, with a significance level of
p=0.1.
The team runs a larger study, of a few thousand customers. The second study shows an
increased average purchase size of $0.74, with a significance level of 0.03. What is your
assessment of this study?
1. The change in purchase size is not practically important, and the good p-value of the second
study is probably a result of the large study size.
2. The change in purchase size is small, but may aggregate up to a large increase in profits over
the entire customer base.
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should run another, larger study.
4. The p-value of the second study shows a statistically significant change in purchase size. The
new website is an improvement.
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Explanation: The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. In
other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor's value are related to changes in the response variable. Conversely, a larger
(insignificant) p-value suggests that changes in the predictor are not associated with changes in the response. Significance of the estimated coefficients: Are the t-statistics greater than 2 in magnitude,
corresponding to p-values less than 0.05 If they are not, you should probably try to refit the model with the least significant variable excluded, which is the "backward stepwise" approach to model refinement.
Remember that the t-statistic is just the estimated coefficient divided by its own standard error. Thus, it measures "how many standard deviations from zero" the estimated coefficient is, and it is used to test the
hypothesis that the true value of the coefficient is non-zero, in order to confirm that the independent variable really belongs in the model.
The p-value is the probability of observing a t-statistic that large or larger in magnitude given the null hypothesis that the true coefficient value is zero. If the p-value is greater than 0.05-which occurs roughly
when the t-statistic is less than 2 in absolute value-this means that the coefficient may be only "accidentally" significant.
There's nothing magical about the 0.05 criterion, but in practice it usually turns out that a variable whose estimated coefficient has a p-value of greater than 0.05 can be dropped from the model without affecting the
error measures very much-try it and see
Related Questions
Question : Refer to the Exhibit.
In the Exhibit. For effective visualization, what is the chart's primary flaw?
1. The slanting of axis labels.
2. The location of the legend.
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4. The order of the columns.
Question : Refer to the exhibit
You have plotted the distribution of savings account sizes for your bank. How would you proceed,
based on this distribution?
1. The data is extremely skewed. Replot the data on a logarithmic scale to get a better sense of it.
2. The data is extremely skewed, but looks bimodal; replot the data in the range 2, 500-10, 000 to be sure.
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4. The data is extremely skewed. Split your analysis into two cohorts: accounts less than 2500, and accounts greater than 2500
Question : Refer to the exhibit.
In the exhibit, a correlogram is provided based on an autocorrelation analysis of a sample dataset.
What can you conclude based only on this exhibit?
1. There appears to be a seasonal component in the data
2. Lag 1 has a significant autocorrelation
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4. There appears to be no structure left to model in the data
Question : Refer to the exhibit.
In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also
in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan,
and the blue represents borrowers that are known to have defaulted on their loan.
Which analytical method could produce the probabilities needed to build this exhibit?
1. Linear Regression
2. Logistic Regression
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4. Association Rules
Question : Refer to the exhibit.
You have created a density plot of purchase
amounts from a retail website as shown. What should
you do next?
1. Recreate the plot using the barplot() function
2. Use the rug() function to add elements to the plot
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4. Reduce the sample size of the purchase amount data used to create the plot
Question : Refer to the exhibit.
You are building a decision tree. In this exhibit, four variables are listed with their respective values
of info-gain.
Based on this information, on which attribute would you expect the next split to be in the decision
tree?
1. Credit Score
2. Age
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4. Gender