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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 10 of 43

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Q91.
What is the advantage of most Big Data systems when it comes to data structure?

a.

They require deep data structure from the source system.

b.

They allow data structure to be added after data arrive at the goal.

c.

They impose strict structural requirements on the source data.

d.

They only work with structured data.

Discuss
Answer: (b).They allow data structure to be added after data arrive at the goal. Explanation:Most Big Data systems allow data structure to be added after data arrive at the goal, making them flexible in terms of data structure.
Q92.
Why should transformations that cause less latency be preferred within the Big Data domain?

a.

To avoid complex data migrations

b.

To optimize data retrieval

c.

To accommodate deep data structures

d.

To ensure linear data growth

Discuss
Answer: (a).To avoid complex data migrations Explanation:Transformations that cause less latency should be preferred within the Big Data domain to avoid complex data migrations when moving data from operational sources to analytical goals.
Q93.
What is the primary consideration for scaling Big Data systems to match data growth patterns?

a.

Adding more central processing units

b.

Optimizing data retrieval

c.

Structured data transformation

d.

Aligning technology and costs with sensible growth

Discuss
Answer: (d).Aligning technology and costs with sensible growth Explanation:The primary consideration for scaling Big Data systems to match data growth patterns is aligning technology and costs with sensible growth to ensure scalability.
Q94.
Which type of scalability involves adding more capacity to a single machine?

a.

Vertical scalability

b.

Horizontal scalability

c.

Linear scalability

d.

Exponential scalability

Discuss
Answer: (a).Vertical scalability Explanation:Vertical scalability involves adding more capacity to a single machine.
Q95.
What does sharding involve in the context of database scalability?

a.

Automatically rebalancing data across multiple machines

b.

Partitioning data across multiple database servers

c.

Increasing data insertion speeds

d.

Horizontal scalability of a single machine

Discuss
Answer: (b).Partitioning data across multiple database servers Explanation:Sharding involves partitioning data across multiple database servers to achieve horizontal scalability.
Q96.
Which layer is considered the most vital among the three layers in the overall infrastructure for many Internet companies?

a.

Ingestion layer

b.

Storage & Processing layer

c.

Serving layer

d.

Metadata management layer

Discuss
Answer: (b).Storage & Processing layer Explanation:The Storage & Processing layer is considered the most vital among the three layers in the overall infrastructure for many Internet companies.
Q97.
What is used as the scalable file system at the bottom of the Storage & Processing layer?

a.

Map-Reduce

b.

Google File System

c.

Dataflow programming framework

d.

Workflow manager

Discuss
Answer: (b).Google File System Explanation:Google File System is used as the scalable file system at the bottom of the Storage & Processing layer.
Q98.
What is the purpose of a dataflow programming framework in the Storage & Processing layer?

a.

Debugging large-scale data

b.

Managing metadata

c.

Performing distributed sorting and hashing

d.

Handling data granularity

Discuss
Answer: (c).Performing distributed sorting and hashing Explanation:A dataflow programming framework in the Storage & Processing layer is used for performing distributed sorting and hashing.
Q99.
Why is debugging large-scale data in Internet firms crucial?

a.

To factor out debugging from the subsystems

b.

To provide a uniform view to users

c.

To maintain consistency in metadata

d.

Due to the presence of different subsystems with varying characteristics

Discuss
Answer: (d).Due to the presence of different subsystems with varying characteristics Explanation:Debugging large-scale data in Internet firms is crucial because there are different subsystems with varying characteristics, making it essential to factor out debugging from these subsystems.
Q100.
What is the purpose of capturing provenance data across the workflow in dealing with different data and process granularity?

a.

To provide a uniform view to users

b.

To integrate metadata from different subsystems

c.

To make inferences across different granularities

d.

To dispatch metadata to a self-governing system

Discuss
Answer: (c).To make inferences across different granularities Explanation:Capturing provenance data across the workflow is done to make inferences across different data and process granularities.
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