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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 2 of 6

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Q11.
What is a significant challenge in data discovery?

a.

Determining data quality

b.

Ensuring data comprehensiveness

c.

Finding high-quality data among vast collections

d.

Analyzing data in real-time

Discuss
Answer: (c).Finding high-quality data among vast collections Explanation:Data discovery is a significant challenge because it involves finding high-quality data from the vast collections of data available on the Web.
Q12.
What challenge involves determining the quality of data sets and their relevance to specific issues?

a.

Data Comprehensiveness

b.

Data Veracity

c.

Data Quality

d.

Data Relevance

Discuss
Answer: (c).Data Quality Explanation:The challenge that involves determining the quality of data sets and their relevance to specific issues is known as "Data Quality."
Q13.
What cautionary note does Paul Miller provide regarding the analysis of Big Data in the healthcare sector?

a.

Healthcare should rely solely on data-driven insights.

b.

Domain experts and common sense should be disregarded.

c.

Responding to flu outbreaks should depend on Google Flu Trends.

d.

Healthcare should not use Big Data for any insights.

Discuss
Answer: (c).Responding to flu outbreaks should depend on Google Flu Trends. Explanation:Paul Miller cautions that it would be worrying if the healthcare sector only responded to flu outbreaks when Google Flu Trends told them to, highlighting the importance of domain experts and common sense.
Q14.
What are some of the main management challenges associated with data warehouses containing sensitive data?

a.

Data storage and retrieval

b.

Data visualization and reporting

c.

Data privacy, security, governance, and ethical considerations

d.

Data transformation and modeling

Discuss
Answer: (c).Data privacy, security, governance, and ethical considerations Explanation:The main management challenges associated with data warehouses containing sensitive data include data privacy, security, governance, and ethical considerations.
Q15.
What does Gray's Laws of Data Engineering suggest in the context of Big Data analysis?

a.

Analyze Big Data before processing it

b.

Scale conventional databases to petabytes

c.

Take the analysis to the data

d.

Rely on conventional wisdom for data analysis

Discuss
Answer: (c).Take the analysis to the data Explanation:Gray's Laws of Data Engineering, adapted for Big Data, suggest taking the analysis to the data, which means bringing the application logic to the data for effective analysis.
Q16.
What is Hadoop primarily used for in the context of Big Data processing?

a.

Storing Big Data

b.

Analyzing structured data

c.

Processing unstructured Big Data

d.

Converting data to relational formats

Discuss
Answer: (c).Processing unstructured Big Data Explanation:Hadoop is primarily used for processing unstructured Big Data, making it suitable for large-scale data processing in the enterprise.
Q17.
What inspired the development of Hadoop?

a.

IBM's research papers

b.

Yahoo's data analysis tools

c.

Google's MapReduce and Google File System (GFS) papers

d.

Facebook's data processing techniques

Discuss
Answer: (c).Google's MapReduce and Google File System (GFS) papers Explanation:Hadoop was inspired by Google's MapReduce and Google File System (GFS) papers, which influenced its development as an open-source platform for analyzing and processing Big Data.
Q18.
What percentage of Yahoo's MapReduce use cases use Pig for data analysis?

a.

Approximately 10%

b.

Approximately 40%

c.

Approximately 60%

d.

Approximately 90%

Discuss
Answer: (c).Approximately 60% Explanation:Approximately 60% of Yahoo's MapReduce use cases use Pig for data analysis.
Q19.
What characterizes advanced users of Hadoop?

a.

They are new users with little expertise in analytics.

b.

They primarily use Hadoop for batch processing.

c.

They often have PhDs and high expertise in analytics and data mining.

d.

They focus on developing simple Hadoop applications.

Discuss
Answer: (c).They often have PhDs and high expertise in analytics and data mining. Explanation:Advanced users of Hadoop often have PhDs and high expertise in analytics, databases, and data mining. They seek to go beyond batch processing and utilize Hadoop for real-time streaming of content.
Q20.
What are some examples of use cases that advanced Hadoop users may focus on?

a.

Data storage and retrieval

b.

Secure application development

c.

Patient outcome predictions and sentiment analysis

d.

Standard protocol development

Discuss
Answer: (c).Patient outcome predictions and sentiment analysis Explanation:Advanced Hadoop users may focus on use cases such as patient outcome predictions, sentiment analysis, product recommendations, ad placements, customer churn, and fraud detection, which can benefit from real-time information.
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