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Welcome to the Application of Big Data in Analyzing Electric Meter Data MCQs Page

Dive deep into the fascinating world of Application of Big Data in Analyzing Electric Meter Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Application of Big Data in Analyzing Electric Meter Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Application of Big Data in Analyzing Electric Meter 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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Application of Big Data in Analyzing Electric Meter Data MCQs | Page 5 of 12

Explore more Topics under Big Data Computing

Q41.
What is the primary advantage of cloud computing for Big Data processing?

a.

Low data complexity

b.

Inefficient resource allocation

c.

Efficient and dynamic resource provision

d.

Lack of standardization

Discuss
Answer: (c).Efficient and dynamic resource provision Explanation:Cloud computing provides efficient and dynamic resource provisioning, which is essential for Big Data processing.
Q42.
Which cloud computing model provides computing resources in a pay-per-use way?

a.

Platform as a Service (PaaS)

b.

Infrastructure as a Service (IaaS)

c.

Software as a Service (SaaS)

d.

Big Data as a Service (BDaaS)

Discuss
Answer: (b).Infrastructure as a Service (IaaS) Explanation:Infrastructure as a Service (IaaS) provides computing resources in a pay-per-use way.
Q43.
What is the benefit of using ready-to-use Big Data services in the cloud?

a.

Lower network access costs

b.

Greater data locality

c.

Elimination of cluster configuration and tuning overhead

d.

Reduced use of APIs

Discuss
Answer: (c).Elimination of cluster configuration and tuning overhead Explanation:Ready-to-use Big Data services in the cloud eliminate the overhead of configuring and tuning your own clusters.
Q44.
What remains a significant issue when transferring data to the cloud for Big Data processing?

a.

Availability of cloud platforms

b.

Data locality

c.

Efficient resource management

d.

Standardization and innovation

Discuss
Answer: (b).Data locality Explanation:Data locality remains a significant issue when transferring data to the cloud for Big Data processing.
Q45.
What technology is responsible for the high-frequency measurements and real-time data processing in the context of energy production and distribution?

a.

Supervisory Control and Data Acquisition (SCADA)

b.

Photovoltaic (PV)

c.

Smart meters

d.

Renewable Energy Sources (RES)

Discuss
Answer: (a).Supervisory Control and Data Acquisition (SCADA) Explanation:Supervisory Control and Data Acquisition (SCADA) systems handle high-frequency measurements and real-time data processing in energy production and distribution.
Q46.
Why do Big Data management techniques become crucial in the management of smart grids with high penetration levels of renewable energy sources (RES)?

a.

To limit the use of SCADA systems

b.

To minimize data collection from sensors

c.

To improve automation algorithms

d.

To manage the unpredictability of RES in real time

Discuss
Answer: (d).To manage the unpredictability of RES in real time Explanation:Big Data management techniques are needed to manage the unpredictability of renewable energy sources (RES) in real time.
Q47.
What is the Intelligent Distributed Data Processing (IDDP), and why is it important in the context of energy production from renewable sources?

a.

IDDP is a technique for reducing data rates in the electricity domain.

b.

IDDP is a method for optimizing energy production from PV plants.

c.

IDDP is a new frontier for addressing challenges related to Big Data in smart grids.

d.

IDDP is used for reducing data transmission times.

Discuss
Answer: (c).IDDP is a new frontier for addressing challenges related to Big Data in smart grids. Explanation:Intelligent Distributed Data Processing (IDDP) is a new frontier for addressing challenges related to Big Data in smart grids, especially in managing renewable energy sources.
Q48.
In the current scenario, what limits the cooperative behavior of photovoltaic (PV) plants in the energy grid?

a.

Lack of suitable PV technology

b.

Unreliable energy production from PV plants

c.

The need for sophisticated optimization techniques

d.

The absence of smart meters

Discuss
Answer: (c).The need for sophisticated optimization techniques Explanation:The cooperative behavior of PV plants is limited by the need for sophisticated optimization techniques that operate with enormous data sets in a short time frame.
Q49.
What is the purpose of installing smart meters at energy production and consumption sites?

a.

To reduce data collection efforts

b.

To supply power independently

c.

To improve the efficiency of power supply and demand management

d.

To avoid the use of smart grids

Discuss
Answer: (c).To improve the efficiency of power supply and demand management Explanation:Smart meters are installed to provide fine-grained information about power use, allowing for more efficient management of power supply and demand.
Q50.
What is the challenge associated with the growing data rates in the electricity domain as the industry transitions toward smart grids?

a.

Data rates are decreasing significantly.

b.

Data increases are manageable with the existing facilities.

c.

Data rates are only doubling or tripling.

d.

Data rates increase by multiple orders of magnitude.

Discuss
Answer: (d).Data rates increase by multiple orders of magnitude. Explanation:The challenge is that data rates increase by multiple orders of magnitude, not simply doubling or tripling the amount of data collected.
Page 5 of 12

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