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Welcome to the Big Data Challenges and Opportunities MCQs Page

Dive deep into the fascinating world of Big Data Challenges and Opportunities with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Challenges and Opportunities, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Data Challenges and Opportunities, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Data Challenges and Opportunities. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Data Challenges and Opportunities. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Data Challenges and Opportunities MCQs | Page 3 of 6

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Q21.
According to John Schroeder, why do many organizations need Hadoop to be easier to use?

a.

To attract top university graduates to their IT organizations

b.

To support real-time streaming of content

c.

To develop complex Hadoop applications

d.

To make it easier to develop, deploy, and run Hadoop applications in a production environment

Discuss
Answer: (d).To make it easier to develop, deploy, and run Hadoop applications in a production environment Explanation:John Schroeder suggests that many organizations need Hadoop to be easier to use in order to simplify the development, deployment, and operation of Hadoop applications in a production environment.
Q22.
What is a common need for new users of Hadoop?

a.

Specialized programming skills

b.

Business-critical and secure applications

c.

Integration with file-based applications

d.

High expertise in analytics

Discuss
Answer: (b).Business-critical and secure applications Explanation:New users of Hadoop often have a need for business-critical and secure applications that can easily integrate with file-based applications and products.
Q23.
What is the primary distinction between data stored in a data warehouse and data stored in Hadoop?

a.

Data warehouse contains trusted data, while Hadoop contains untrusted data.

b.

Data warehouse contains unstructured data, while Hadoop contains structured data.

c.

Data warehouse is based on the divide and conquer parallelism model, while Hadoop uses shared-nothing architecture.

d.

Data warehouse is inspired by Google GFS, while Hadoop is inspired by relational databases.

Discuss
Answer: (a).Data warehouse contains trusted data, while Hadoop contains untrusted data. Explanation:Data stored in a data warehouse is typically structured and considered "trusted," whereas data stored in Hadoop is often semistructured or unstructured and may not be considered trusted.
Q24.
What is one of the key distinctions between the development of Big Data in the database world and in the systems world?

a.

Big Data in the database world is based on open-source technologies, while Big Data in the systems world relies on proprietary software.

b.

Big Data in the database world focuses on structured data, while Big Data in the systems world deals with unstructured and semistructured data.

c.

Big Data in the database world was primarily driven by large web companies, while Big Data in the systems world emerged from traditional enterprises.

d.

Big Data in the database world emphasizes data volume scalability, while Big Data in the systems world prioritizes hardware size scalability.

Discuss
Answer: (b).Big Data in the database world focuses on structured data, while Big Data in the systems world deals with unstructured and semistructured data. Explanation:One of the key distinctions is that Big Data in the database world primarily deals with structured data, while Big Data in the systems world deals with unstructured and semistructured data.
Q25.
What does the parallel database software stack typically comprise?

a.

SQL, relational dataflow layer, and column storage manager

b.

SQL compiler, relational dataflow layer, and row storage manager

c.

Hadoop MapReduce, Hadoop Distributed File System, and HiveQL

d.

SQL, PigLatin, and Hadoop M/R job

Discuss
Answer: (b).SQL compiler, relational dataflow layer, and row storage manager Explanation:The parallel database software stack typically comprises SQL compiler, relational dataflow layer, and row storage manager.
Q26.
Which tool is part of the Hadoop software stack?

a.

SQL

b.

HiveQL

c.

Relational Dataflow Layer

d.

Row Storage Manager

Discuss
Answer: (b).HiveQL Explanation:HiveQL is part of the Hadoop software stack.
Q27.
What is a key requirement when handling Big Data, particularly in terms of scalability?

a.

Hardware complexity

b.

Data consistency

c.

Concurrency management

d.

Data volume scalability

Discuss
Answer: (d).Data volume scalability Explanation:A key requirement when handling Big Data is data volume scalability, which refers to the ability to handle large volumes of data effectively.
Q28.
What is the primary advantage of using NoSQL data stores in handling structured and unstructured data in a two-system configuration?

a.

Reduced maintenance complexity

b.

Lower Total Cost of Ownership (TCO)

c.

Improved data transfer efficiency

d.

Enhanced data security

Discuss
Answer: (a).Reduced maintenance complexity Explanation:One of the primary advantages of using NoSQL data stores in a two-system configuration is reduced maintenance complexity.
Q29.
In the context of Big Data, what does the term "scaling up" refer to?

a.

Adding independent nodes to a system

b.

Increasing data transfer efficiency

c.

Relying on Moore's Law for continuous scaling

d.

Buying a bigger box to handle larger data volumes

Discuss
Answer: (d).Buying a bigger box to handle larger data volumes Explanation:In the context of Big Data, "scaling up" refers to buying a bigger box or increasing the hardware capacity to handle larger data volumes.
Q30.
What is the main purpose of Enterprise Search in the context of Big Data?

a.

Implementing a proprietary full-text search engine

b.

Searching structured data only

c.

Searching multiple types of data generated by an enterprise

d.

Analyzing unstructured data

Discuss
Answer: (c).Searching multiple types of data generated by an enterprise Explanation:Enterprise Search in the context of Big Data implies the ability to search multiple types of data generated by an enterprise, both structured and unstructured.
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