Welcome to the Hadoop Frameworks MCQs Page

Dive deep into the fascinating world of Hadoop Frameworks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Hadoop Frameworks, a crucial aspect of Hadoop. In this section, you will encounter a diverse range of MCQs that cover various aspects of Hadoop Frameworks, 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 Hadoop.

Check out the MCQs below to embark on an enriching journey through Hadoop Frameworks. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Hadoop.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Hadoop Frameworks. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Hadoop Frameworks MCQs | Page 15 of 20

Explore more Topics under Hadoop

01

Basics of Hadoop

02

MapReduce Basics

03

Hadoop Distributed File System

04

Hadoop IO

05

MapReduce Development and Features

06

Introduction to Pig

07

Hive and HBase

08

ZooKeeper,Sqoop and Cassandra

09

Hadoop Incubators and Hadoop Development Tools

10

Hadoop Libraries and Utilities

Q141.

Point out the correct statement.

Apache Hama is a distributed computing framework based on Bulk Synchronous Parallel computing techniques for massive scientific computations

Hama is a Top Level Project under the Apache Software Foundation

BSP stands for Bulk Synchronous Parallel

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q142.

Point out the wrong statement.

The major difference between Hadoop and Hama is map/reduce tasks can’t communicate with each other

Hama follows master/slave pattern

A JobTracker maps to a BSPMaster, TaskTracker maps to a GroomServer and Map/Reduce task maps to a BSPTask

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q143.

Point out the correct statement.

HCat provides connectors for MapReduce

Apache HCatalog provides table data access for CDH components such as Pig and MapReduce

HCat makes Hive metadata available to users of other Hadoop tools like Pig, MapReduce and Hive

All of the mentioned

Discuss

Answer: (b).Apache HCatalog provides table data access for CDH components such as Pig and MapReduce

Q144.

Point out the wrong statement.

HCatalog is a table and storage management layer for Hadoop that enables users with different data processing tools

There is Hive-specific interface for HCatalog

Data is defined using HCatalog’s command line interface (CLI)

All of the mentioned

Discuss

Answer: (b).There is Hive-specific interface for HCatalog

Q145.

Point out the correct statement.

There is no guaranteed read consistency when a partition is dropped

Unpartitioned tables effectively have one default partition that must be created at table creation time

Once a partition is created, records cannot be added to it, removed from it, or updated in it

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q146.

Point out the wrong statement.

The original name of WebHCat was Templeton

Robert in client management uses Hive to analyze his clients’ results

With HCatalog, HCatalog cannot send a JMS message that data is available

All of the mentioned

Discuss

Answer: (c).With HCatalog, HCatalog cannot send a JMS message that data is available

Q147.

Point out the correct statement.

The HCatLoader and HCatStorer interfaces are used with Pig scripts to read and write data in HCatalog-managed tables

HCatalog is not thread safe

HCatLoader is used with Pig scripts to read data from HCatalog-managed tables.

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q148.

Point out the wrong statement.

The Hive metastore lets you create tables without specifying a database

Restrictions apply to the types of columns HCatLoader can read from HCatalog-managed tables

If the table is partitioned, you can indicate which partitions to scan by immediately following the load statement with a partition filter statement

None of the mentioned

Discuss

Answer: (d).None of the mentioned

Q149.

Point out the correct statement.

Mahout is distributed under a commercially friendly Apache Software license

Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop® and using the MapReduce paradigm

Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms

None of the mentioned

Discuss

Answer: (d).None of the mentioned

Q150.

Point out the wrong statement.

‘Taste’ collaborative-filtering recommender component of Mahout was originally a separate project and can run standalone without Hadoop

Integration of Mahout with initiatives such as the Pregel-like Giraph are actively under discussion

Calculating the LLR is very straightforward

None of the mentioned

Discuss

Answer: (d).None of the mentioned

Page 15 of 20

Welcome to the Hadoop Frameworks MCQs Page

Hadoop Frameworks MCQs | Page 15 of 20

Explore more Topics under Hadoop

Basics of Hadoop

MapReduce Basics

Hadoop Distributed File System

Hadoop IO

MapReduce Development and Features

Introduction to Pig

Hive and HBase

ZooKeeper,Sqoop and Cassandra

Hadoop Incubators and Hadoop Development Tools

Hadoop Libraries and Utilities

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