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MCQs | Big Data Analytics – Hadoop Introduction, Ecosystem and Its Components

Big Data Analytics | Hadoop Introduction, Ecosystem MCQs: This section contains the Multiple-Choice Questions & Answers on Big Data Analytics - Hadoop Introduction, Ecosystem with explanations.
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Big Data Analytics Hadoop Introduction, Ecosystem and Its Components MCQs

1. _____ is a platform for developing data flows for the extraction, transformation, and loading (ETL) of huge datasets, as well as for data analysis.

Spark
HBase
Hive
Pig

Answer: D) Pig

Explanation:

Pig is a high-level platform or tool that is used to process massive amounts of data at a high level. When processing via the MapReduce framework, it provides a high level of abstraction for the user. It includes a high-level scripting language, known as Pig Latin that is used to construct the data analysis scripts that are employed in the system.

2. In contrast to relational databases, Hive is a query engine that supports the elements of SQL that are specifically designed for querying data.

True
False

Answer: A) True

Explanation:

Apache Hadoop is a distributed computing platform that allows for simple data summarization, ad hoc searches, and the analysis of big datasets stored in a variety of databases and file systems that connect with Hadoop. It is an easy approach to provide structure to massive volumes of unstructured data and then conduct batch SQL-like queries on that data using Hive as a data warehouse.

3. Custom extensions built in the ____ programming language are also supported by Hive.

Java
C#
C
C++

Answer: A) Java

Explanation:

Custom extensions built in the Java programming language is also supported by Hive. Apache Hive is built on top of Apache Hadoop and is used to provide data query and analysis capabilities to users. In order to query data stored in multiple databases and file systems that are integrated with Hadoop, Hive provides a SQL-like interface.

4. Amongst which of the following is / are correct,

Hive is a relational database that supports SQL queries.
Pig is a relational database that supports SQL queries.
Both A and B
None of the mentioned above

Answer: C) Both A and B

Explanation:

Hive and Pig are the relational database that supports SQL queries. There is a special language similar to SQL known as HiveQL that converts the queries into MapReduce programmes that can be executed on datasets in HDFS. Pig provides the use of nested data types- Tuples, Maps, Bags, etc. and supports data operations like Joins, Filters, and Ordering.

5. In order to analyze all of this Big Data, Hive is a tool that has been developed.

True
False

Answer: A) True

Explanation:

In the context of big data analytics, Apache Hive is a distributed, fault-tolerant data warehousing system that can handle huge amounts of data. A data warehouse is a centralized repository of information that can be easily evaluated in order to make data-driven decisions that are informed. Hive lets users to read, write, and manage petabytes of data using SQL, which makes it a powerful tool for data scientists.

6. ____ general-purpose model and runtime framework for distributed data analytics.

Mapreduce
Spark
Hive
All of the mentioned above

Answer: A) Mapreduce

Explanation:

MapReduce is a programming model which enables massive scalability across hundreds or thousands of servers in a Hadoop cluster. MapReduce is known as the heart and soul of Apache Hadoop because of its processing and computing power.

7. Scalability is prioritized over latency in jobs such as _____.

HBase
HDFS
Hive
Mapreduce

Answer: C) Hive

Explanation:

Scalability is prioritized over latency in Hive. The performance of queries is influenced by the size of the cluster and the volume of data. In most cases, increasing cluster capacity alleviates problems caused by memory limits or disc performance limitations. Larger clusters, on the other hand, are more prone to experience various types of scalability challenges, such as a single slow node that causes query performance concerns.

8. ______ node serves as the Slave and is responsible for carrying out the Tasks that have been assigned to it by the JobTracker.

TaskReduce
Mapreduce
TaskTracker
JobTracker

Answer: C) TaskTracker

Explanation:

TaskTracker node serves as the Slave and is responsible for carrying out the Tasks that have been assigned to it by the JobTracker.

9. Apache Hive is data storage and ______ that stores and organizes data for study and querying.

Querying tool
Mapper
MapReduce
All of the mentioned above

Answer: A) Querying tool

Explanation:

Apache Hive is data storage and Querying tool that stores and organizes data for study and querying. A hive is a data transformation tool. It gathers information from a variety of sources, primarily HDFS. Hive is a good storage tool for the Hadoop Framework and is included with the framework. Hive is a replica of relational management tables that is used for data storage. It is capable of storing both organized and unstructured data. Hive, on the other hand, is capable of storing data. Hive imports unstructured data from HDFS first, and then develops a structure around it before loading the data.

10. The MapReduce framework is responsible for processing one or more pieces of data and producing the output results as ______.

Maptask
Task execution
Mapper
All of the mentioned above

Answer: A) Maptask

Explanation:

The MapReduce framework is responsible for processing one or more pieces of data and producing the output results as Maptask. A Map Task is a single instance of the MapReduce software that runs in the background. These jobs are responsible for determining which records from a data block should be processed. The input data is split and examined in parallel using computer resources that have been assigned to it in a Hadoop cluster.

11. Apache Hive is a data ______ infrastructure that is built on top of the Hadoop platform.

Warehouse
Map
Reduce
None of the mentioned above

Answer: A) Warehouse

Explanation:

Apache Hive is a data warehouse infrastructure built on the Hadoop framework that is ideal for data summarization, analysis, and querying. It is available as a free download. It makes use of a SQL-like language known as HQL (Hive Query Language).

12. The Hadoop framework is built in Java, which means that MapReduce applications do not need to be written in _____.

C#
C
Java
None of the mentioned above

Answer: C) Java

Explanation:

Hadoop is an Apache open-source framework developed in Java that enables for the distributed processing of massive datasets across clusters of computers by employing simple programming concepts. Hadoop is available for free from the Apache Software Foundation. A distributed storage and computation environment is provided by the Hadoop framework application, which works in conjunction with clusters of computers to deliver its services.

13. _____ maps input key/value pairs to a set of intermediate key/value pairs.

Reducer
Mapper
File system
All of these

Answer: B) Mapper

Explanation:

Mapper maps input key/value pairs to a set of intermediate key/value pairs. Map-Reduce is a programming methodology that is primarily divided into two parts, which are referred to as the Map Phase and the Reduce Phase, respectively. It is intended for the processing of data in parallel, which is distributed across a number of nodes.

14. HQL is a query language that is used to construct the custom map-reduce framework in Hive, which is written in ______.

Java
PHP
C#
None of the mentioned above

Answer: A) Java

Explanation:

HQL is a query language that is used to construct the custom map-reduce framework in Hive, which is written in Java. Hive supports a SQL parlance known as Hive Query Language (HQL) to retrieve or modify the data. Which is stored in the Hadoop.

15. The _______ is the default partitioned in Hadoop, and it offers a method called getPartition that allows us to partition data.

HashPartitioner
Map function
Reduce function
All of the mentioned above

Answer: A) HashPartitioner

Explanation:

The HashPartitioner is the default partitioned in Hadoop, and it offers a method called getPartition that allows us to partition data. When a MapReduce job is being executed, the partitioner does the partitioning of the keys of the intermediate map-outputs. The partition is created with the help of the hash function and the key (or a subset of the key). When all divisions are added together, it equals the total number of decrease tasks.

16. Hadoop is a framework that can be used in conjunction with a number of related products. Among the most common cohorts are ______.

MapReduce, Hive and HBase
Hive, Spark and HBase
Spark, Hive and ZooKeeper
Spark, HBase and Hive

Answer: A) MapReduce, Hive and HBase

Explanation:

Hadoop is a framework that can be used with a number of related products. Among the most common cohorts are MapReduce, Hive and HBase. The Hadoop software library is a framework that enables for the distributed processing of massive data sets across clusters of computers using simple programming models. It is a component of the Apache Hadoop software library. It is intended to grow from a small number of servers to thousands of devices, each of which can do computing and storage on its own.

17. ______ is best described as a programming model that is used to construct Hadoop-based applications that can be scaled up and down.

Oozie
Zookepper
MapReduce
All of the mentioned above

Answer: C) MapReduce

Explanation:

MapReduce is best described as a programming model that is used to construct Hadoop-based applications that can be scaled up and down. When it comes to the Hadoop framework, MapReduce is a programming paradigm or pattern that is used to retrieve large amounts of data stored in the Hadoop File System.

18. Amongst which of the following is/are the Hive function Meta commands.

Show functions
Describe function
Both A and B
None of the mentioned above

Answer: C) Both A and B

Explanation:

Show functions and Describe function and are the Hive function Meta commands.

19. _____ is a shell utility that can be used to run Hive queries in either interactive or batch mode, depending on the situation.

$HIVE_HOME/bin/hive
$HIVE/bin/
$HIVE_HOME/hive
All of the mentioned above

Answer: A) $HIVE_HOME/bin/hive

Explanation:

$HIVE_HOME/bin/hive is a shell utility that can be used to run Hive queries in either interactive or batch mode, depending on the situation.

20. The _____ tool has the capability of listing all of the possible database schemas.

sqoop-list-databases
Hbase-list
hive schema
sqoop-list-columns

Answer: A) sqoop-list-databases

Explanation:

The sqoop-list-databases tool has the capability of listing all of the possible database schemas. The Sqoop List Databases tool is a database server query execution and parsing tool that runs and parses the "SHOW DATABASES" query. This displays a list of every database that is currently present on the database server. The primary goal of this utility is to compile a list of all of the database schemas that are currently available on the server.

21. Amongst which of the following is/are true with reference to User-defined Functions of Hive.

function that fetches one or more columns from a row as arguments
It returns a single value
Both A and B
None of the mentioned above

Answer: C) Both A and B

Explanation:

User-defined Functions of Hive are the functions that fetch one or more columns from a row as arguments. It returns a single value. These functions give us the ability to design custom functions that process individual records or groups of related records. Hive comes pre-loaded with a large number of useful functionalities. Although there is some exclusion, there are also some specific circumstances for which UDFs are the best option.

22. Amongst which of the following is/are correct.

Default location of Hadoop configuration is in $HADOOP /conf/ HOME
If $HADOOP HOME is specified, Sqoop will utilise the default installation location
default location of Hadoop configuration is in $HADOOP HOME/conf/
Sqoop command-line tool serves as a wrapper for the bin/hadoop script that is included with Hadoop as a base.

Answer: D) Sqoop command-line tool serves as a wrapper for the bin/hadoop script that is included with Hadoop as a base

Explanation:

Sqoop command-line tool serves as a wrapper for the bin/hadoop script that is included with Hadoop as a base. Sqoop provides a straightforward command line, allowing us to retrieve data from a variety of databases using sqoop commands. They are written in Java and communicate with other databases through the JDBC interface. In addition to being an open-source technology, it also stands for "SQL to Hadoop" and "Hadoop to SQL."

23. A _____ serves as the master, and each cluster has just one NameNode.

Data Node
Block Size
Data block
NameNode

Answer: D) NameNode

Explanation:

A NameNode serves as the master, and each cluster has just one NameNode. Managing the File System Namespace and controlling access to files by clients are the responsibilities of the NameNode, which is a very highly available server.

24. HDFS always needs to work with large data sets.

True
False

Answer: A) True

Explanation:

HDFS always needs to work with large data sets. It will not be fruitful if HDFS is deployed to process several small data sets ranging in some MB or GB. The architecture of HDFS is designed in such a way so that it can be best fitted to store and retrieve huge amount of data. What are required are high cumulative data bandwidth and the scalability feature to spread out from a single node cluster to a hundred or a thousand-node cluster. The acid test is that HDFS should be able to manage tens of millions of files in a single occurrence.

25. HDFS operates in a ____ manner.

Master-slave architecture
Master-worker architecture
Worker-slave architecture
All of the mentioned above

Answer: B) Master-worker architecture

Explanation:

HDFS operates in a Master-worker architecture manner. It indicates that there is a single master node and a number of worker nodes in a given cluster. The Namenode is the node that serves as the master node. The namenode is the master node that runs on a different node in the cluster from the rest of the nodes.

26. HDFS follows the write-once, read-many.

True
False

Answer: A) True

Explanation:

HDFS follows the write-once, read-many approach for its files and applications. It assumes that a file in HDFS once written will not be modified, though it can be access ‘n’ number of times. At present, in HDFS strictly has one writer at any time. This assumption enables high throughput data access and also simplifies data coherency issues.

27. Amongst which of the following is not aligns as a characteristic of HDFS?

HDFS file system is well suited for storing data associated with applications that require low latency data access.
HDFS is well-suited for storing data connected to applications that require low-latency data access to be performed.
HDFS is not suited for instances in which multiple/simultaneous writes to the same file are required.
None of the mentioned above

Answer: C) HDFS is not suited for instances in which multiple/simultaneous writes to the same file are required.

Explanation:

HDFS is extremely fault-tolerant and to be implemented on low-cost hardware, this feature makes this best choice to cloud storage. A high-throughput access to application data is provided by HDFS, which is well-suited for applications that deal with massive amounts of data.

28. In order to interact with HDFS, a command line interface named _____ is provided.

HDFS Shell
DFS Shell
K Shell
FS Shell

Answer: D) FS Shell

Explanation:

It is possible to view the contents of a directory indicated in the path provided by the user by using the Hadoop FS shell command ls. All FS shell commands accept URIs for path names as inputs. Scheme:/authority/path is the URI format used in this example. In the case of HDFS, the scheme is hdfs, and in the case of the Local File System, the scheme is file. The scheme as well as the authority is entirely optional. Otherwise, the default scheme supplied in the configuration will be utilized if it is not explicitly specified.

29. HDFS stores data in a distributed manner, the data can be processed in parallel on a _____ of nodes.

Cluster
Data Node
Master Node
None of the mentioned above

Answer: A) Cluster

Explanation:

HDFS stores data in a distributed manner, the data can be processed in parallel on a cluster of nodes. This, plus data locality, cut the processing time and enable high throughput. With HDFS, computation happens on the DataNodes where the data resides, rather than having the data move to where the computational unit is. By minimizing the distance between the data and the computing process, this approach decreases network congestion and boosts a system's overall throughput.

30. With reference to HDFS, Name Node is the prime node which contains metadata.

True
False

Answer: A) True

Explanation:

Name Node is the prime node which contains metadata (data about data) requiring comparatively fewer resources than the data nodes that stores the actual data. These data nodes are commodity hardware in the distributed environment. HDFS maintains all the coordination between the clusters and hardware, thus working at the heart of the system.

MCQs | Big Data Analytics – Fundamentals

MCQs | Big Data Analytics – Descriptive Analytics

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