Which of the following is a key feature of Apache Spark?
Question
Which of the following is a key feature of Apache Spark?
Solution
Apache Spark is a powerful open-source processing engine built around speed, ease of use, and sophisticated analytics. Here are some key features of Apache Spark:
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Speed: Apache Spark is known for its speed. It can process large volumes of data much faster than other platforms because it uses in-memory computing technologies. It can also perform batch processing 100 times faster than MapReduce.
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Ease of Use: Spark provides high-level APIs in Java, Scala, Python and R, and an interactive shell in Scala and Python. This makes it easier for developers to create and run applications.
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Advanced Analytics: Spark not only supports 'Map' and 'Reduce' operations but also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms.
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Fault Tolerance: Spark uses the Resilient Distributed Dataset (RDD) which is a fault-tolerant collection of elements that can be processed in parallel.
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Multiple Format Support: Spark supports a diverse range of data sources such as Parquet, JSON, Hive and others.
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Real-Time Processing: Spark can handle real-time data processing. It can process live streams of data and generate results in real-time.
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Scalability: Spark can handle large amounts of data and can scale from a single server to thousands of machines.
These are some of the key features that make Apache Spark a preferred choice for big data and analytics.
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1.Question 1What are the three main components of Apache Spark architecture?1 pointScala; Java; PythonData; compute interface; resource managementStorage; HDFS; PythonMesos; YARN; Kubernetes2.Question 2What are DataFrames in Apache Spark?1 pointDataFrames is a distributed file system in Spark used for storing large data sets efficiently.DataFrames are a distributed collection of data organized into named columns.DataFrames are Spark’s built-in machine learning models for predictive analytics.DataFrames is a data format for storing graph data structures in Spark.3.Question 3What is Apache Spark?1 pointHardware manufacturerIn-memory framework for distributed data processingCloud storage serviceClosed-source data analysis tool4.Question 4What is functional programming?1 pointA programming approach that emphasizes the how to of the solution as opposed to the what of the solutionA programming approach that focuses solely on graphical functions and visual designs A programming method that prioritizes procedural programming over the use of mathematical functionsA style of programming that follows the mathematical function format5.Question 5Which of the following statements defines Resilient Distributed Datasets (RDDs)? Select all that apply.1 pointRDD is a collection of fault-tolerant elements.RDD is capable of receiving parallel operations.RDDs are immutable.RDD is a distributed database management system.6.Question 6What is the primary purpose of parallel programming?1 pointTo employ specific control and coordination mechanismTo run noncontemporary instructionsTo use multiple compute resources to solve a computational problemTo break a problem into discrete parts that can be solved sequentially7.Question 7Which of the following is a benefit of DataFrames?1 pointTo scale from kilobytes of data on multiple laptops to petabytes on a large clusterTo scale small-scale data on a laptopSupports specific data formats and storage systemsTo scale from kilobytes of data on a single laptop to petabytes on a large cluster
1.Question 1An Apache Spark application has two processes: the driver program and the executor. The driver process can be run on which of the following? 1 pointTaskCluster nodeWorker nodeExecutors2.Question 2Which cluster manager is most suitable for establishing simple clusters?1 pointApache MesosKubernetesSpark StandaloneApache Hadoop YARN3.Question 3The 'spark-submit' script, included with Spark for submitting applications, offers various options/settings. Which option/setting allows you to view available options for a specific cluster manager?1 point`deploy-mode``--class <full-class-name>``--executor-cores` `./bin/spark-submit --help` 4.Question 4What is the advantage of using Spark on IBM Cloud? Select all that apply.1 pointPre-existing default configurationEasy to configure local cluster nodes Enterprise-grade securityBetter communication for local cluster nodes 5.Question 5Spark dynamic configuration avoids hardcoding specific values. Which of the following is an example where dynamic configuration can be appropriately used?1 pointApplication versionSpecifying the number of cores to be utilizedApplication nameProperties related to the application6.Question 6Which of the following statements accurately describes the method used for configuring Spark?1 pointConfiguration Type: PropertiesParameters: Adjust and control application behaviorConfiguration Type: LoggingParameters: Adjust settings on a per-machine basisConfiguration Type: PropertiesParameters: Adjust settings on a per-machine basisConfiguration Type: LoggingParameters: Adjust and control application behavior7.Question 7Which of the following statements is accurate regarding the two driver-deploy modes?1 pointClient mode refers to the mode when the application submitter launches the driver process outside the cluster.Client mode refers to the mode when the framework launches the driver process inside the cluster.Cluster mode refers to the mode when the application submitter launches the driver process outside the cluster.Cluster mode refers to the mode when the application submitter launches the executor process outside the cluster.
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