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Welcome to the Big Data Exploration MCQs Page

Dive deep into the fascinating world of Big Data Exploration with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Exploration, 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 Exploration, 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 Exploration. 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 Exploration. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Data Exploration MCQs | Page 1 of 9

Explore more Topics under Big Data Computing

Q1.
What is the main challenge posed by the era of data deluge?

a.

Lack of data

b.

Slow data production

c.

Data exploration

d.

Efficient Big Data query processing

Discuss
Answer: (d).Efficient Big Data query processing Explanation:The main challenge posed by the era of data deluge is the need for efficient Big Data query processing.
Q2.
Which field is expected to collect multiple terabytes of data on a daily basis?

a.

Social networks

b.

Web log analysis

c.

Astronomy

d.

Scientific experiments

Discuss
Answer: (c).Astronomy Explanation:Scientific analysis such as astronomy is expected to collect multiple terabytes of data on a daily basis.
Q3.
What are some potential benefits for businesses in analyzing Big Data?

a.

Increased data collection

b.

Faster query processing

c.

Better customer satisfaction and services

d.

Decreased user interactions

Discuss
Answer: (c).Better customer satisfaction and services Explanation:Fast Big Data analysis for businesses can lead to better customer satisfaction and services.
Q4.
Why is speed emphasized as crucial for businesses dealing with Big Data?

a.

To reduce data collection

b.

To adapt services to user needs

c.

To decrease the amount of data collected

d.

To ensure data trends remain stable

Discuss
Answer: (b).To adapt services to user needs Explanation:Speed is crucial for businesses dealing with Big Data to adapt their services quickly to user needs.
Q5.
What is one of the challenges faced by sciences in dealing with Big Data?

a.

Slow data analysis

b.

Lack of data collection

c.

Data exploration

d.

Data management

Discuss
Answer: (d).Data management Explanation:Sciences struggle with data management when dealing with Big Data.
Q6.
What is one of the fundamental problems associated with Big Data?

a.

Lack of available data

b.

Difficulty in data collection

c.

Inability to consume and make sense of data fast enough

d.

Excessive time spent on data tuning

Discuss
Answer: (c).Inability to consume and make sense of data fast enough Explanation:One of the fundamental problems associated with Big Data is the inability to consume and make sense of data fast enough.
Q7.
Why is it challenging to tune modern database systems for Big Data?

a.

Because there is always enough workload knowledge available

b.

Because there is ample idle time to tune the system

c.

Because Big Data arrives quickly and unpredictably, requiring quick reactions

d.

Because database systems are designed to consume only a portion of the data

Discuss
Answer: (c).Because Big Data arrives quickly and unpredictably, requiring quick reactions Explanation:Tuning modern database systems for Big Data is challenging because Big Data arrives quickly and unpredictably, requiring quick reactions.
Q8.
What is the main assumption behind the design of traditional database systems?

a.

The need to consume only a portion of the data

b.

The availability of expert knowledge

c.

The requirement to provide correct and complete answers

d.

The focus on data tuning

Discuss
Answer: (c).The requirement to provide correct and complete answers Explanation:Traditional database systems are designed with the main assumption that they should always consume all data to provide correct and complete answers.
Q9.
Why is it difficult to tune a database system for Big Data exploration?

a.

Because it requires a significant amount of time and expert knowledge

b.

Because exploration processes lead to unpredictable queries

c.

Because installation processes are complex

d.

Because workload knowledge is readily available

Discuss
Answer: (b).Because exploration processes lead to unpredictable queries Explanation:Tuning a database system for Big Data exploration is difficult because exploration processes lead to unpredictable queries, and we may not know exactly what kind of queries to pose in advance.
Q10.
What is one critical side effect of using Big Data clusters for scalability?

a.

Reduced energy consumption

b.

Efficient utilization of resources

c.

Wasted resources when consuming unnecessary data

d.

Enhanced exploration of data

Discuss
Answer: (c).Wasted resources when consuming unnecessary data Explanation:Using Big Data clusters for scalability can lead to wasted resources when consuming data that is not really necessary for the exploration path.
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