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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 34 of 43

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Q331.
What is the primary programming model used in Hadoop for distributed data processing?

a.

SQL

b.

Map-Reduce

c.

JavaScript

d.

Python

Discuss
Answer: (b).Map-Reduce Explanation:The primary programming model used in Hadoop for distributed data processing is Map-Reduce.
Q332.
What is one of the key advantages of Hadoop's HDFS (Hadoop Distributed File System)?

a.

It is optimized for real-time data processing.

b.

It provides a schema for managing data.

c.

It offers high availability and fault tolerance.

d.

It uses a traditional relational database model.

Discuss
Answer: (c).It offers high availability and fault tolerance. Explanation:One of the key advantages of Hadoop's HDFS is its high availability and fault tolerance.
Q333.
Which Google technologies inspired the development of Hadoop?

a.

Google Drive and Gmail

b.

Google Maps and YouTube

c.

Google Search and Google Photos

d.

Google MapReduce and Google File System (GFS)

Discuss
Answer: (d).Google MapReduce and Google File System (GFS) Explanation:Hadoop was inspired by Google's MapReduce and Google File System (GFS) technologies.
Q334.
What is the basic idea behind HadoopDB?

a.

To create a pure parallel database system

b.

To combine the features of parallel databases and Map-Reduce for hybrid processing

c.

To develop a distributed key-value store

d.

To optimize network protocols and hardware

Discuss
Answer: (b).To combine the features of parallel databases and Map-Reduce for hybrid processing Explanation:The basic idea behind HadoopDB is to combine the features of parallel databases and Map-Reduce for hybrid processing.
Q335.
What is the role of Hive in HadoopDB?

a.

It serves as the primary storage layer.

b.

It translates SQL queries into Map-Reduce jobs.

c.

It optimizes network communication.

d.

It handles fault tolerance in HadoopDB.

Discuss
Answer: (b).It translates SQL queries into Map-Reduce jobs. Explanation:Hive in HadoopDB serves as the translation layer, translating SQL queries into Map-Reduce jobs.
Q336.
What is HBase built on top of?

a.

MySQL

b.

Hadoop HDFS

c.

Oracle Database

d.

MongoDB

Discuss
Answer: (b).Hadoop HDFS Explanation:HBase is built on top of Hadoop HDFS (Hadoop Distributed File System).
Q337.
What type of database is HBase?

a.

Relational database

b.

Nonrelational database

c.

Document-oriented database

d.

Graph database

Discuss
Answer: (b).Nonrelational database Explanation:HBase is a nonrelational database.
Q338.
How does HBase handle updates to its indices?

a.

It uses a single mutating B-tree for each index.

b.

It periodically flushes all updates to disk.

c.

It maintains a mutable index on disk.

d.

It creates a new index for each update.

Discuss
Answer: (b).It periodically flushes all updates to disk. Explanation:HBase handles updates to its indices by periodically flushing all updates to disk.
Q339.
What approach does HBase use for writing data to disk?

a.

B-tree approach

b.

Log Structured Merge Tree approach

c.

Bitmap indexing approach

d.

NoSQL approach

Discuss
Answer: (b).Log Structured Merge Tree approach Explanation:HBase uses the Log Structured Merge Tree approach for writing data to disk.
Q340.
What technique can be used to further improve HBase's performance?

a.

Index sharding

b.

Data partitioning

c.

Bloom filters

d.

Schema normalization

Discuss
Answer: (c).Bloom filters Explanation:Bloom filters can be used to further improve HBase's performance.
Page 34 of 43

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