Big Data Analytics Quiz!

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Samy Boulos, MSc (Computer Science) |
Data Engineer
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Samy Boulos is an experienced Technology Consultant with a diverse 25-year career encompassing software development, data migration, integration, technical support, and cloud computing. He leverages his technical expertise and strategic mindset to solve complex IT challenges, delivering efficient and innovative solutions to clients.
 , MSc (Computer Science)
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  • 1/15 Questions

    What license is Apache Hadoop distributed under?

    • Apache License 2.0
    • Shareware
    • Mozilla Public License
    • Commercial
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About This Quiz

Do you know about Big Data Analytics? To check your knowledge and understanding on the same, you can take this Big Data Analytics Quiz. Big Data Analytics is a process in which complex or too large data is analyzed or systematically extracted to get the data sets. It is mostly used where traditional data processing is used. This quiz will not only test your knowledge but also help you learn new things. All the best, and do share your result!

Big Data Analytics Quiz! - Quiz

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  • 2. 

    Which type of data Hadoop can deal with is

    • Structured

    • Semi-structured

    • Unstructured

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    Hadoop is capable of dealing with structured, semi-structured, and unstructured data. Structured data refers to data that is organized in a fixed format, such as data stored in relational databases. Semi-structured data refers to data that does not have a fixed format but contains some organizational elements, such as XML or JSON files. Unstructured data refers to data that does not have any specific organization or format, such as text documents, images, or videos. Hadoop's distributed processing framework allows it to handle and analyze all types of data, making it a versatile tool for big data processing.

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  • 3. 

    Which of the following is a component of Hadoop?

    • YARN

    • HDFS

    • MapReduce

    • None of the above

    Correct Answer
    A. None of the above
    Explanation
    All of the options mentioned (YARN, HDFS, MapReduce) are components of Hadoop. YARN (Yet Another Resource Negotiator) is the resource management layer of Hadoop, responsible for managing and allocating resources to applications. HDFS (Hadoop Distributed File System) is the distributed file system used by Hadoop to store and retrieve data. MapReduce is the programming model used by Hadoop for processing and analyzing large datasets in parallel across a cluster of computers. Therefore, all three options are correct components of Hadoop.

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  • 4. 

    Hadoop Framework is written in

    • Python

    • Java

    • C++

    • Scala

    Correct Answer
    A. Java
    Explanation
    The correct answer is Java because Hadoop is a framework that is primarily written in Java. Java provides the necessary tools and libraries to handle large-scale data processing and distributed computing, which are the core functionalities of Hadoop. Additionally, Java's object-oriented nature and platform independence make it a suitable choice for developing a framework like Hadoop that can run on various operating systems and hardware configurations.

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  • 5. 

    Which of the following is the daemon of Hadoop?

    • NameNode

    • Node manager

    • DataNode

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    The correct answer is "All of the above" because in Hadoop, there are three main daemons: NameNode, Node Manager, and DataNode. The NameNode is responsible for managing the metadata of the Hadoop Distributed File System (HDFS). The Node Manager is responsible for managing resources and scheduling tasks on each individual node. The DataNode is responsible for storing and retrieving data in HDFS. Therefore, all three options mentioned are valid daemons in Hadoop.

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  • 6. 

    Which of the following platforms does Apache Hadoop run on?

    • Bare metal

    • Unix-like

    • Cross-platform

    • Debian

    Correct Answer
    A. Cross-platform
    Explanation
    Apache Hadoop is a framework that is designed to run on various platforms, making it cross-platform. It is not limited to a specific operating system or hardware, allowing it to be deployed on different environments such as Windows, Linux, and macOS. This flexibility enables organizations to leverage Hadoop's capabilities regardless of their existing infrastructure, making it a popular choice for big data processing and analysis.

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  • 7. 

    Which one of the following is false about Hadoop?

    • It is a distributed framework.

    • The main algorithm used in it is Map Reduce.

    • It runs with commodity hardware.

    • All are true.

    Correct Answer
    A. All are true.
    Explanation
    The statement "All are true" means that all of the given options are true about Hadoop. This implies that Hadoop is indeed a distributed framework, it utilizes the Map Reduce algorithm as its main algorithm, and it is capable of running on commodity hardware.

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  • 8. 

    Hadoop works in

    • Master-worker fashion

    • Master–slave fashion

    • Worker/slave fashion

    • All of the mentioned

    Correct Answer
    A. Master–slave fashion
    Explanation
    Hadoop works in a master-slave fashion, where there is a single master node that manages and coordinates the overall operations, and multiple slave nodes that perform the actual data processing tasks. The master node assigns tasks to the slave nodes and collects the results from them. This architecture allows for distributed and parallel processing, making Hadoop a scalable and efficient framework for big data processing.

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  • 9. 

    The archive file created in Hadoop has the extension of

    • .hrh

    • .har

    • .hrc

    • .hrar

    Correct Answer
    A. .har
    Explanation
    The correct answer is .har.

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  • 10. 

    Apache Hadoop achieves reliability by replicating the data across multiple hosts and hence does not require ________ storage on hosts.

    • Standard RAID levels

    • RAID

    • ZFS

    • Operating system

    Correct Answer
    A. RAID
    Explanation
    Apache Hadoop achieves reliability by replicating the data across multiple hosts and hence does not require RAID storage on hosts. RAID (Redundant Array of Independent Disks) is a data storage technology that combines multiple physical disk drives into a single logical unit to improve performance and data redundancy. However, Hadoop achieves reliability through data replication across multiple hosts, eliminating the need for RAID storage on individual hosts.

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  • 11. 

    Which of the following is the correct statement?

    • Data locality means moving computation to data instead of data to computation.

    • Data locality means moving data to computation instead of computation to data.

    • Both the above.

    • None of the above.

    Correct Answer
    A. Data locality means moving computation to data instead of data to computation.
    Explanation
    Data locality refers to the practice of bringing the computation closer to the data it operates on, rather than moving the data to where the computation is happening. This approach improves performance and efficiency by reducing the amount of data transfer and network communication required. By moving the computation to the data, it avoids the overhead of moving large amounts of data across a network, which can be time-consuming and resource-intensive. Therefore, the correct statement is that data locality means moving computation to data instead of data to computation.

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  • 12. 

    Which of the following properties gets configured on mapred-site.xml ?

    • Replication factor

    • Java Environment variables.

    • Directory names to store HDFS files.

    • Host and port where MapReduce job runs.

    Correct Answer
    A. Host and port where MapReduce job runs.
    Explanation
    The property that gets configured on mapred-site.xml is the host and port where the MapReduce job runs. This configuration allows the system to know where to execute the MapReduce tasks and where to send the results back. It is important to correctly configure this property to ensure that the MapReduce jobs are executed on the desired hosts and ports.

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  • 13. 

    Which of the below apache system deals with ingesting streaming data to Hadoop?

    • Flume

    • Oozie

    • Hive

    • Kafka

    Correct Answer
    A. Flume
    Explanation
    Flume is the correct answer because it is an Apache system specifically designed for ingesting streaming data to Hadoop. Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data from various sources into Hadoop for analysis and processing. It provides a flexible and scalable architecture that allows data ingestion from multiple sources and delivers it to Hadoop in a reliable and efficient manner.

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  • 14. 

    Which statement is false about Hadoop?

    • It runs with commodity hardware.

    • It is a part of the Apache project sponsored by the ASF.

    • It is best for live streaming of data.

    • None of the above.

    Correct Answer
    A. It is best for live streaming of data.
    Explanation
    Hadoop is a framework that is known for its ability to process and store large amounts of data across a cluster of computers using commodity hardware. It is a part of the Apache project sponsored by the ASF, which means it is an open-source software developed by a community of contributors. However, Hadoop is not specifically designed for live streaming of data. While it can handle real-time data processing to some extent, there are other technologies like Apache Kafka or Apache Flink that are better suited for live streaming applications.

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  • 15. 

    Which command is used to check the status of all daemons running in the HDFS?

    • Jps

    • Fsck

    • Distcp

    • None of the above

    Correct Answer
    A. Jps
    Explanation
    The command "jps" is used to check the status of all daemons running in the HDFS. Jps stands for Java Virtual Machine Process Status Tool, and it is used to list all Java processes running on a machine. By running the "jps" command, it will display the names and process IDs of all Java processes, including the HDFS daemons, such as the NameNode, DataNode, and SecondaryNameNode. Therefore, "jps" is the correct command to check the status of all daemons running in the HDFS.

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Samy Boulos |MSc (Computer Science) |
Data Engineer
Samy Boulos is an experienced Technology Consultant with a diverse 25-year career encompassing software development, data migration, integration, technical support, and cloud computing. He leverages his technical expertise and strategic mindset to solve complex IT challenges, delivering efficient and innovative solutions to clients.

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  • Current Version
  • Feb 07, 2024
    Quiz Edited by
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    Expert Reviewed by
    Samy Boulos
  • Mar 21, 2020
    Quiz Created by
    Sharavana
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