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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 1 of 6

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Q1.
What is the definition of "Big Data" according to McKinsey Global Institute (MGI)?

a.

Datasets beyond the ability of typical database software tools to capture.

b.

Extremely large datasets.

c.

Datasets with high complexity.

d.

Datasets beyond 1 petabyte in size.

Discuss
Answer: (a).Datasets beyond the ability of typical database software tools to capture. Explanation:The definition of "Big Data" according to McKinsey Global Institute (MGI) is that it refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition emphasizes that Big Data is characterized not just by its size but by the challenges it presents to traditional database tools.
Q2.
What is the main source of value derived from Big Data, according to MGI?

a.

The size of the dataset.

b.

Creating transparencies.

c.

Discovering needs and exposing variability.

d.

Human decision-making.

Discuss
Answer: (c).Discovering needs and exposing variability. Explanation:The main source of value derived from Big Data, as explained by MGI, includes creating transparencies (making data more visible), discovering needs and exposing variability (identifying patterns and variations in data), segmenting customers (dividing customers into groups for targeted strategies), and replacing/supporting human decision-making with automated algorithms (using data-driven insights for decision-making and innovation).
Q3.
What is the concept of "Big Data Search" as described by David Gorbet?

a.

Searching for large volumes of data.

b.

Combining data explorations with analytics.

c.

Using a single formula for extracting value from Big Data.

d.

Hiding insights from data explorations.

Discuss
Answer: (b).Combining data explorations with analytics. Explanation:The concept of "Big Data Search" as described by David Gorbet implies that there is no single formula for extracting value from Big Data; it depends on the application. It involves the ability to search through large volumes of complex data from multiple sources via interactive queries, which can provide organizations with new insights about their products, customers, and services. Combining these interactive data explorations with analytics and visualization can uncover hidden insights.
Q4.
In Big Data analytics, what is the main challenge related to talent, as identified by MGI?

a.

Shortage of data storage solutions

b.

Shortage of data privacy regulations

c.

Shortage of talent to take advantage of Big Data

d.

Shortage of computing power

Discuss
Answer: (c).Shortage of talent to take advantage of Big Data Explanation:The main challenge related to talent in Big Data analytics, as identified by MGI, is the shortage of talent necessary to take advantage of Big Data.
Q5.
What is the expected volume of data by the year 2020, according to IBM?

a.

800,000 PB

b.

10 TB

c.

35 ZB

d.

6 billion mobile phones

Discuss
Answer: (c).35 ZB Explanation:The expected volume of data by the year 2020 is estimated to reach 35 ZB (zettabytes), according to IBM.
Q6.
Which social media platform generates approximately 7+ terabytes (TB) of data every day?

a.

LinkedIn

b.

Twitter

c.

Instagram

d.

Snapchat

Discuss
Answer: (b).Twitter Explanation:Twitter generates approximately 7+ terabytes (TB) of data every day.
Q7.
What percentage of today's information is considered unstructured?

a.

Less than 10%

b.

About 50%

c.

Approximately 80%

d.

Nearly 100%

Discuss
Answer: (c).Approximately 80% Explanation:More than 80% of today's information is considered unstructured.
Q8.
Why is it challenging to analyze and manage unstructured data in Big Data?

a.

Unstructured data is easy to represent in rows and columns.

b.

Unstructured data is generated within the organization.

c.

Unstructured data is typically small in size.

d.

Unstructured data has complex structures that are hard to represent, and organizations want to combine and analyze data from various sources.

Discuss
Answer: (d).Unstructured data has complex structures that are hard to represent, and organizations want to combine and analyze data from various sources. Explanation:Analyzing and managing unstructured data in Big Data is challenging because unstructured data often has complex structures that are hard to represent, and organizations want to combine and analyze data from various sources.
Q9.
What challenge arises when businesses want real-time analytics and increased user access to data?

a.

Volume

b.

Velocity

c.

Variety

d.

Veracity

Discuss
Answer: (b).Velocity Explanation:The challenge that arises when businesses want real-time analytics and increased user access to data is known as "Velocity."
Q10.
What challenges are associated with data quality and data availability?

a.

Volume and Velocity

b.

Veracity and Velocity

c.

Variety and Volume

d.

Velocity and Veracity

Discuss
Answer: (b).Veracity and Velocity Explanation:The challenges associated with data quality and data availability are related to "Veracity" (data quality) and "Velocity" (data availability).
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