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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 2 of 7

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Answer: (b).To divide large processing tasks into smaller, independent steps Explanation:The primary goal of the pipes-and-filters design pattern is to divide large processing tasks into smaller, independent steps connected by channels.
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Answer: (c).To expose a simple interface for processing messages Explanation:The primary function of each filter in the pipes-and-filters pattern is to expose a simple interface for processing messages.
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Answer: (b).Through pipes that send messages from one filter output to the next filter input Explanation:Filters are connected in the pipes-and-filters pattern through pipes that send messages from one filter output to the next filter input.
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Answer: (a).The Unix operating system's pipe operator "|" Explanation:One well-known implementation of the pipes-and-filters pattern is the Unix operating system's pipe operator "|" which connects processes that read data from the standard input stream and write results to the standard output stream.
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Answer: (c).Analyzing complex series of interrelated events in modern distributed information systems Explanation:CEP (Complex Event Processing) involves analyzing complex series of interrelated events in modern distributed information systems.
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Answer: (d).A possibly infinite temporal sequence of discrete events Explanation:An event stream in the context of Complex Event Processing (CEP) is a possibly infinite temporal sequence of discrete events.
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Answer: (c).As simple numeric payloads with time stamps Explanation:In the spChains framework, events are defined as data structures (objects) carrying a simple numeric payload with time stamps.
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Answer: (c).To apply complex computations on event streams Explanation:The primary purpose of CEP operators (queries) is to apply complex computations on event streams.
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Answer: (c).Intervals defined by the occurrence of certain events or a time period Explanation:Time windows in the context of CEP are intervals defined by the occurrence of certain events or a time period.
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Answer: (b).Sliding or moving windows and batch windows Explanation:The two most important variants of time windows in CEP are sliding or moving windows and batch windows.
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