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Welcome to the Introduction to Big Data MCQs Page

Dive deep into the fascinating world of Introduction to Big Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Introduction to Big Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Introduction to Big Data, 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 Introduction to Big Data. 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 Introduction to Big Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Introduction to Big Data MCQs | Page 24 of 43

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Q231.
Why is sophisticated scheduling necessary in Big Data solutions?

a.

To minimize the use of parallel processing.

b.

To eliminate Service Level Agreements (SLAs).

c.

To ensure that computational processes run for an unlimited time.

d.

To efficiently manage the execution of parallel computational tasks.

Discuss
Answer: (d).To efficiently manage the execution of parallel computational tasks. Explanation:Sophisticated scheduling is necessary in Big Data solutions to efficiently manage the execution of parallel computational tasks.
Q232.
What is the purpose of a Service Level Agreement (SLA) in the context of computational processes in Big Data solutions?

a.

To define the schedule for data backups.

b.

To allocate more resources to computational processes.

c.

To specify the terms and conditions for computational services.

d.

To eliminate the need for scheduling computational processes.

Discuss
Answer: (c).To specify the terms and conditions for computational services. Explanation:The purpose of a Service Level Agreement (SLA) in the context of computational processes in Big Data solutions is to specify the terms and conditions for computational services.
Q233.
How can cloud solutions assist in implementing dynamic computational solutions for Big Data?

a.

By limiting the computational power of the infrastructure.

b.

By reducing the need for parallel processing.

c.

By providing elastic cloud capabilities for dynamic computational tasks.

d.

By prioritizing single-threaded computational processes.

Discuss
Answer: (c).By providing elastic cloud capabilities for dynamic computational tasks. Explanation:Cloud solutions can assist in implementing dynamic computational solutions for Big Data by providing elastic cloud capabilities for dynamic computational tasks.
Q234.
How are Big Data processes typically formalized?

a.

Through manual documentation and note-taking.

b.

Through handwritten scripts.

c.

In the form of process workflows.

d.

Through informal discussions.

Discuss
Answer: (c).In the form of process workflows. Explanation:Big Data processes are typically formalized in the form of process workflows.
Q235.
What role do automation systems play in managing Big Data workflows?

a.

They are used to manually execute each step in the workflow.

b.

They are not suitable for processing Big Data workflows.

c.

They automate the creation, organization, and transfer of workflows.

d.

They rely on handwritten scripts for workflow automation.

Discuss
Answer: (c).They automate the creation, organization, and transfer of workflows. Explanation:Automation systems in Big Data workflows automate the creation, organization, and transfer of workflows.
Q236.
Why might traditional Workflow Management Systems (WfMS) be inadequate for processing Big Data in real time?

a.

They are highly scalable and efficient.

b.

They lack support for programing languages like Java and JavaScript.

c.

They use a "store-then-process" paradigm.

d.

They can handle complex event processing (CEP).

Discuss
Answer: (c).They use a "store-then-process" paradigm. Explanation:Traditional Workflow Management Systems (WfMS) are inadequate for processing Big Data in real time because they use a "store-then-process" paradigm, which may not meet the high data flow and timing requirements of some Big Data applications.
Q237.
What is the main characteristic of complex event processing (CEP) in the context of Big Data?

a.

It relies on a "store-then-process" paradigm.

b.

It processes events on the fly without storage.

c.

It uses traditional Workflow Management Systems (WfMS).

d.

It is not suitable for handling high data flow.

Discuss
Answer: (b).It processes events on the fly without storage. Explanation:Complex event processing (CEP) in the context of Big Data processes events on the fly without storage.
Q238.
How does the Large Hadron Collider (LHC) handle the massive amount of data it generates?

a.

It stores all data locally at CERN.

b.

It uses a worldwide computing grid for data distribution and processing.

c.

It relies on Tier-3 computing centers.

d.

It restricts access to data to a select group of researchers.

Discuss
Answer: (b).It uses a worldwide computing grid for data distribution and processing. Explanation:The Large Hadron Collider (LHC) uses a worldwide computing grid for data distribution and processing, connecting multiple computing centers worldwide to support data collection, storage, processing, and analysis.
Q239.
Why is the cloud paradigm considered a desirable feature in Big Data solutions?

a.

It provides unlimited storage and computing power.

b.

It offers advanced data analytics tools.

c.

It is resistant to natural disasters.

d.

It is a cost-effective solution.

Discuss
Answer: (a).It provides unlimited storage and computing power. Explanation:The cloud paradigm is considered a desirable feature in Big Data solutions because it provides seemingly unlimited storage space and computing power.
Q240.
What is a limitation of using public cloud systems for extensive computations on large volumes of data?

a.

High cost

b.

Low bandwidth

c.

Geographical dispersion

d.

Data mobility

Discuss
Answer: (b).Low bandwidth Explanation:A limitation of using public cloud systems for extensive computations on large volumes of data is their low bandwidth.
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