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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 1 of 7

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Q1.
What is gaining new momentum in Ambient Intelligence (AmI) research as smart environments like smart buildings become a reality?

a.

Machine learning

b.

Social media analytics

c.

Big Data issues

d.

Internet of Things (IoT)

Discuss
Answer: (c).Big Data issues Explanation:Big Data issues are gaining new momentum in AmI research as smart environments like smart buildings become a reality.
Q2.
What are some of the challenges associated with the deployment of smart environments, especially in large-scale settings?

a.

Custom data sampling and representation

b.

Standard, fast, and effective solutions

c.

Low data granularity

d.

Lack of sensors and actuators

Discuss
Answer: (a).Custom data sampling and representation Explanation:Some of the challenges associated with the deployment of smart environments in large-scale settings include the need to move from custom data sampling and representation to standard, fast, and effective solutions.
Q3.
What type of test environment are smart buildings considered to be for Big Data technologies?

a.

A low-demand environment

b.

A controlled environment

c.

A highly demanding environment

d.

A low-cardinality environment

Discuss
Answer: (c).A highly demanding environment Explanation:Smart buildings are considered to be a highly demanding test environment for Big Data technologies.
Q4.
Why is data aggregation and summarization important in the context of smart buildings?

a.

To increase data granularity

b.

To reduce data cardinality

c.

To identify anomalous situations

d.

To perform short-term adaptation and forecasting

Discuss
Answer: (b).To reduce data cardinality Explanation:Data aggregation and summarization are important in the context of smart buildings to reduce data cardinality.
Q5.
What characterizes the data generated in a smart building, particularly in terms of data granularity and cardinality?

a.

Consistent time frames and low cardinality

b.

Diverse time frames and low cardinality

c.

Consistent time frames and high cardinality

d.

Diverse time frames and high cardinality

Discuss
Answer: (d).Diverse time frames and high cardinality Explanation:Data generated in a smart building is characterized by diverse time frames (milliseconds to minutes or hours) and high cardinality due to the large number of sensors deployed in various locations.
Q6.
What is the primary advantage of using complex event processing (CEP) in addressing Big Data issues in the context of smart buildings?

a.

CEP offers low granularity data processing

b.

CEP requires minimal technical knowledge

c.

CEP provides reliable, fast, and cost-effective solutions

d.

CEP is designed for single-event processing

Discuss
Answer: (c).CEP provides reliable, fast, and cost-effective solutions Explanation:The primary advantage of using complex event processing (CEP) is that it offers reliable, fast, and cost-effective solutions for handling high-throughput and high-granularity data streams.
Q7.
What is a significant challenge in adopting CEP engines for smart buildings?

a.

Limited availability of CEP engines

b.

A lack of skilled stream-processing experts

c.

Incompatibility with industrial settings

d.

Lack of support for low-level query writing

Discuss
Answer: (b).A lack of skilled stream-processing experts Explanation:A significant challenge in adopting CEP engines for smart buildings is that quite in-depth knowledge of the inner processing mechanisms and engines is required, which may not be readily available among smart building professionals.
Q8.
Why are direct CEP queries often unsuited for industrial or large-scale settings in smart buildings?

a.

They are too expensive

b.

They are too slow

c.

They are not reusable

d.

They do not support data aggregation

Discuss
Answer: (c).They are not reusable Explanation:Direct CEP queries are often unsuited for industrial or large-scale settings in smart buildings because they are not reusable, and the ability to reuse validated processes is crucial for cost reduction.
Q9.
What is the primary goal of the spChains Big Data elaboration framework in the context of smart buildings?

a.

To replace CEP engines with a more efficient solution

b.

To increase the data granularity in smart buildings

c.

To simplify the writing of CEP queries for monitoring processes

d.

To reduce the performance of event processing in smart buildings

Discuss
Answer: (c).To simplify the writing of CEP queries for monitoring processes Explanation:The primary goal of the spChains Big Data elaboration framework in the context of smart buildings is to simplify the writing of CEP queries for monitoring processes.
Q10.
How does the spChains framework address the need for reusable and modular processing in smart buildings?

a.

It relies on complex CEP queries

b.

It uses a pipes-and-filter composition pattern

c.

It requires extensive knowledge of CEP systems

d.

It focuses on low-level query writing

Discuss
Answer: (b).It uses a pipes-and-filter composition pattern Explanation:The spChains framework addresses the need for reusable and modular processing in smart buildings by using a pipes-and-filter composition pattern, where processing blocks can be cascaded to obtain complex elaboration chains.
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