Intelligent Apache Spark Test

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| By Melomee
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Melomee
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Quizzes Created: 23 | Total Attempts: 88,916
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  • 1/10 Questions

    Which of these languages is NOT supported by Spark for developing big data applications?

    • Python
    • Java
    • Scala
    • Groovy
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About This Quiz

Spark is a registered trademark of Apache Software Foundation; it is one of the most popularly known frameworks for computing clusters. Now, let's see how knowledgeable you are when it comes to Apache Spark.

Intelligent Apache Spark Test - Quiz

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  • 2. 
    How can you use Spark to access and analyze data stored in Cassandra databases? - ProProfs

    How can you use Spark to access and analyze data stored in Cassandra databases?

    • By using Spark Special Keys

    • By using Scala

    • By using Sparse Vector

    • By using Spark Cassandra Connector

    Correct Answer
    A. By using Spark Cassandra Connector
    Explanation
    The Spark Cassandra Connector is a library that allows Spark to access and analyze data stored in Cassandra databases. It provides an interface between Spark and Cassandra, allowing users to read and write data from and to Cassandra using Spark's DataFrame API. This connector enables efficient data transfer between Spark and Cassandra, allowing for seamless integration and analysis of data stored in Cassandra databases using Spark's powerful analytics capabilities.

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  • 3. 
    What is the full meaning of RDD?  - ProProfs

    What is the full meaning of RDD? 

    • Resilient Distinctive Datasets

    • Resilient Diagonal databases

    • Resilient Distributed Datasets

    • Responsive Distributed Databases

    Correct Answer
    A. Resilient Distributed Datasets
    Explanation
    RDD stands for Resilient Distributed Datasets. This term refers to a fundamental data structure in Apache Spark, which is a distributed computing system. RDDs are fault-tolerant and immutable collections of objects that can be processed in parallel across a cluster of computers. They allow users to perform various operations on the data, such as transformations and actions. Therefore, the correct answer is Resilient Distributed Datasets.

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  • 4. 
    How can you describe RDDs? - ProProfs

    How can you describe RDDs?

    • Mutable

    • Immutable

    • Positive

    • Negative

    Correct Answer
    A. Immutable
    Explanation
    RDDs (Resilient Distributed Datasets) are a fundamental data structure in Apache Spark, and they are described as immutable. This means that once an RDD is created, its data cannot be modified. Instead, any transformations applied to an RDD create a new RDD, leaving the original RDD unchanged. This immutability is a key characteristic of RDDs, as it allows for efficient and fault-tolerant distributed processing. Additionally, immutability enables Spark to perform optimizations such as lazy evaluation and lineage tracking, which enhance performance and fault recovery capabilities.

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  • 5. 
    How many cluster managers are in Spark?  - ProProfs

    How many cluster managers are in Spark? 

    • 1

    • 2

    • 3

    • 4

    Correct Answer
    A. 3
    Explanation
    Spark has three cluster managers: Standalone, YARN, and Mesos. Each cluster manager has its own advantages and can be used based on the specific requirements of the application. Standalone is the simplest cluster manager and is suitable for small-scale deployments. YARN is a widely used cluster manager that is integrated with Hadoop ecosystem, making it a good choice for big data processing. Mesos provides fine-grained resource allocation and is known for its scalability and fault-tolerance. Therefore, the correct answer is 3.

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  • 6. 
    Which of the following is not a Spark cluster manager? - ProProfs

    Which of the following is not a Spark cluster manager?

    • YARN

    • Standalone deployment

    • Groovy

    • Apache Mesos

    Correct Answer
    A. Groovy
    Explanation
    Groovy is a programming language and not a Spark cluster manager. Spark cluster managers are responsible for allocating resources and scheduling tasks in a Spark cluster. YARN, Standalone deployment, and Apache Mesos are all valid cluster managers that can be used with Spark.

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  • 7. 
    To connect Spark with Mesos, which of these must the location of Spark binary packages be to Mesos? - ProProfs

    To connect Spark with Mesos, which of these must the location of Spark binary packages be to Mesos?

    • Close

    • Far

    • Accessible

    • Inaccessible

    Correct Answer
    A. Accessible
    Explanation
    In order to connect Spark with Mesos, the location of Spark binary packages must be accessible to Mesos. This means that Mesos should be able to reach and access the Spark binary packages without any restrictions or limitations. This accessibility ensures that Mesos can properly utilize and integrate with Spark for efficient data processing and resource management.

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  • 8. 
    What is the representation of dependencies in-between RDDs called?  - ProProfs

    What is the representation of dependencies in-between RDDs called? 

    • Graph

    • Quadratic graph

    • Quadratic graph

    • Lineage graph

    Correct Answer
    A. Lineage graph
    Explanation
    Lineage graph is the representation of dependencies between RDDs. It shows the history of transformations that have been applied to the RDDs and allows for fault tolerance by enabling RDDs to be reconstructed in case of data loss or failure. The lineage graph helps in optimizing the execution of RDD operations by allowing the system to track the dependencies and efficiently schedule the tasks.

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  • 9. 
    What do you trigger by setting up a ‘spark.cleaner.ttl’ parameter?  - ProProfs

    What do you trigger by setting up a ‘spark.cleaner.ttl’ parameter? 

    • Automatic delete

    • Automatic cleanup

    • Automatic recovery

    • Automatic recycling

    Correct Answer
    A. Automatic cleanup
    Explanation
    By setting up the 'spark.cleaner.ttl' parameter, you trigger automatic cleanup in Spark. This parameter specifies the time-to-live (TTL) for cached data and metadata in Spark. When the TTL expires, Spark automatically cleans up and removes the expired data and metadata from memory, freeing up resources for other computations. This helps in efficient memory management and prevents memory overflow in Spark applications.

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  • 10. 
    Which is described as a sequence of Resilient Distributed Databases that represent a stream of data?  - ProProfs

    Which is described as a sequence of Resilient Distributed Databases that represent a stream of data? 

    • Dstream

    • YARN

    • HDFS

    • BlinkDB

    Correct Answer
    A. Dstream
    Explanation
    Dstream is described as a sequence of Resilient Distributed Databases that represent a stream of data. It is a high-level abstraction provided by Apache Spark Streaming, which allows for the processing of real-time streaming data. Dstream stands for Discretized Stream, and it represents a continuous stream of data divided into small batches or RDDs (Resilient Distributed Datasets) for processing. This allows for the efficient and parallel processing of streaming data in a distributed manner.

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  • Current Version
  • Mar 21, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • May 04, 2018
    Quiz Created by
    Melomee

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