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Welcome to the Real Time Big Data Processing MCQs Page

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

Real Time Big Data Processing MCQs | Page 7 of 7

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Q61.
Which CEP engine is currently used for the base block library implementation in the spChains framework?

a.

Apache Kafka

b.

Esper

c.

Flink

d.

Storm

Discuss
Answer: (b).Esper Explanation:The base block library implementation in the spChains framework currently uses the Esper CEP engine.
Q62.
What is the primary reason for the increasing interest in effective data handling and management?

a.

To increase data security

b.

Due to the growth in distributed sensors and devices

c.

To reduce the need for middleware

d.

To enhance application user interfaces

Discuss
Answer: (b).Due to the growth in distributed sensors and devices Explanation:The increasing interest in effective data handling and management is primarily due to the growth in distributed sensors and devices contributing to various processes.
Q63.
How does Solar handle sensors and applications in the context of data streams?

a.

Solar treats sensors as consumers and applications as publishers.

b.

Sensors and applications are not part of the Solar system.

c.

Solar treats sensors as data stream publishers and applications as data stream consumers.

d.

Solar provides a separate infrastructure for sensors and applications.

Discuss
Answer: (c).Solar treats sensors as data stream publishers and applications as data stream consumers. Explanation:Solar treats sensors as data stream publishers and applications as data stream consumers, enabling developers to compose desired sensor streams and transform low-level data into meaningful context using operators.
Q64.
What distinguishes spChains from the Solar system in terms of stream processing blocks?

a.

Solar defines a standard set of stream processing blocks, while spChains offers custom-developed solutions.

b.

Solar and spChains use completely different block libraries.

c.

Solar uses highly efficient stream processing engines, while spChains relies on if-then-else rules.

d.

Solar allows developers to design processing operators, while spChains provides a standard set of basic processing blocks.

Discuss
Answer: (d).Solar allows developers to design processing operators, while spChains provides a standard set of basic processing blocks. Explanation:spChains aims to provide a standard, yet extensible, set of basic processing blocks, ensuring reusability across different application domains. In contrast, Solar allows developers to design and implement processing operators.
Q65.
What is the primary goal of the spChains framework?

a.

To achieve the highest possible global query optimization

b.

To provide a small set of predefined processing blocks

c.

To support mastering of CEP queries in a simplified manner

d.

To focus on pattern detection in stream processing

Discuss
Answer: (c).To support mastering of CEP queries in a simplified manner Explanation:The primary goal of the spChains framework is to support mastering of Complex Event Processing (CEP) queries in a simplified, yet effective, manner based on stream processing block composition.
Q66.
What is the peak processing performance of the spChains framework in terms of events per second?

a.

Around 10,000 events per second

b.

Around 50,000 events per second

c.

Around 100,000 events per second

d.

Around 200,000 events per second

Discuss
Answer: (c).Around 100,000 events per second Explanation:The peak processing performance of the spChains framework is around 100,000 events per second.
Q67.
How does the spChains framework support the data processing life cycle in industrial environments in the future?

a.

By providing extensive deep learning capabilities

b.

By focusing on pattern-based block composition

c.

By integrating chain/block hot-plugging functionalities

d.

By developing standalone chain definition interfaces

Discuss
Answer: (c).By integrating chain/block hot-plugging functionalities Explanation:The spChains framework plans to integrate chain/block hot-plugging functionalities to better support the data processing life cycle in industrial environments.
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