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Dell EMC Data Science and BigData Certification Questions and Answers



Question : You have been assigned to run a logistic regression model for each of countries, and all the
data is currently stored in a PostgreSQL database. Which tool/library would you use to produce
these models with the least effort?
: You have been assigned to run a logistic regression model for each of countries, and all the
1. RStudio
2. MADlib
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4. HBase

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Explanation: MADlib is an open-source library for scalable in-database analytics. It offers dataparallel implementations of mathematical, statistical, and machine learning methods for structured and
unstructured data. Because MADlib is designed and built to accommodate massive parallel processing of
data, MADlib is ideal for Big Data in-database analytics. MADlib supports the opensource database PostgreSQL as well as the Pivotal Greenplum Database and Pivotal
HAWQ. HAWQ is a SQL query engine for data stored in the Hadoop Distributed File System (HDFS).
Module Description
Generalized Linear Models : Includes linear regression, logistic regression, and multinomial logistic regression









Question : Imagine you are trying to hire a Data Scientist for your team. In addition to technical ability and
quantitative background, which additional essential trait would you look for in people applying for
this position?


: Imagine you are trying to hire a Data Scientist for your team. In addition to technical ability and
1. Communication skill
2. Scientific background
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4. Well Organized



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Explanation: let's discuss how you can be on your way to be an effective Data Scientist.

1. Diverse Technologies - a good Data Scientist is handy with a collection of open-source tools - Hadoop, Java, Python, among others. Knowing when to use those tools, and how to code, are prerequisites. To be a
Data Scientist, you should have your hands on a number of tools and technologies, especially open source ones, such as Hadoop, Java, Python, C++, ECL, etc. Besides, having good understanding of database technologies,
such as NoSQL database like HBase, CouchDB, etc. is an add-on.

2. Mathematics - The second skill, as you might expect, is a base in statistics, algorithms, machine learning, and mathematics. Conventional computer science degrees no longer satisfy the quest of a data scientist.
The job requires someone who on the one hand understands large-scale machine learning algorithms and programming and on the other is a statistician. So, the profile is better suited for experts in other scientific and
mathematical disciplines, apart from computer science.

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understanding business requirements, application requirements and interpret the patterns and relationships mined from data to people in marketing group, product development teams, and corporate executives. And all
this requires good business skills, to get the things done right.

4. Visualization - The fourth set of skills focus on making products real and making data available to users. In other words, this one's a combination of coding skills, an ability to see where data can add value,
and collaborating with teams to make these products a reality. You may be able to mine and model data, but are you able to visualize it? Well if not, mind that you should be able to work with some, at least a few of
the data visualization tools. Some of these include Tableau, Flare, D3.js, Processing, Google Visualization API, and Raphael.js.

5. Innovation - You don't just have to look around and do with data. You got to think creative, and innovate. A data scientist should be eager to learn more, be curious to find new things, and think out of the box.
They should be focused on making products real and making perfectly done data available to users. They should be able to see where data can add value, and how it can brings better results.

6. Problem-Solving Skills This may seem obvious, of course, because data science is all about solving problems. But a good data scientist must take the time to learn what problem needs to be solved, how the
solution will deliver value, and how it'll be used and by whom.

7. Communications Skills - Communication is the key to work with various cross-functional team members and present analytics in a compelling and effective manner to the leadership and customers. In other words, you
may be brilliant in your rarefied field, but you're not going to be a really good data scientist if you can't communicate with the common folk.






Question : What describes the use of UNION clause in a SQL statement?
: What describes the use of UNION clause in a SQL statement?
1. Operates on queries and potentially decreases the number of rows
2. Operates on queries and potentially increases the number of rows
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4. Operates on both tables and queries and potentially increases both the number of rows and columns



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Explanation: The SQL UNION clause/operator is used to combine the results of two or more SELECT statements without returning any duplicate rows.

To use UNION, each SELECT must have the same number of columns selected, the same number of column expressions, the same data type, and have them in the same order, but they do not have to be the same length.

Syntax:
The basic syntax of UNION is as follows:

SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]

UNION

SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]



Related Questions

Question : A data scientist wants to predict the probability of death from heart disease based on three risk
factors: age, gender, and blood cholesterol level.
What is the most appropriate method for this project?

 : A data scientist wants to predict the probability of death from heart disease based on three risk
1. Linear regression
2. K-means clustering
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4. Apriori algorithm





Question : What are the characteristics of Big Data?

 : What are the characteristics of Big Data?
1. Data type, processing complexity, and data structure variety.
2. Data volume, business importance, and data structure variety.
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4. Data volume, processing complexity, and business importance




Question : You are analyzing data in order to build a classifier model. You discover non-linear data and
discontinuities that will affect the model. Which analytical method would you recommend?

 : You are analyzing data in order to build a classifier model. You discover non-linear data and
1. Logistic Regression
2. Decision Trees
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4. ARIMA



Question : What is an appropriate data visualization to use in a presentation for a project sponsor?

 : What is an appropriate data visualization to use in a presentation for a project sponsor?
1. Box and Whisker plot
2. Pie chart
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4. Density plot


Question : In a Student's t-test, what is the meaning of the p-value?

 : In a Student's t-test, what is the meaning of the p-value?
1. it is the "power" of the Student's t-test
2. it is the mean of the distribution for the null hypothesis
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4. it is the area under the appropriate tails of the Student's distribution



Question : In addition to less data movement and the ability to use larger datasets in calculations, what is a
benefit of analytical calculations in a database?

 : In addition to less data movement and the ability to use larger datasets in calculations, what is a
1. improved connections between disparate data sources
2. more efficient handling of categorical values
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4. full use of data aggregation functionality