MCQs on Setting Up Apache Spark | Apache Spark MCQs Questions

Apache Spark is an open-source distributed computing framework designed for big data processing and machine learning tasks. This chapter explores the essentials of setting up Apache Spark, including installation, configuration, and integration with Hadoop. It also delves into using Spark Shell with popular languages like Scala, Python, and R, as well as understanding the Spark UI for monitoring applications. These Apache Spark MCQs questions are designed to test your foundational knowledge and prepare you for real-world implementations and certifications.

Multiple-Choice Questions (MCQs)

Installing Apache Spark Locally

1. Which language is Apache Spark primarily written in?
   a) Java
   b) Scala
   c) Python
   d) R
2. What is required to install Apache Spark on a local machine?
   a) Java Development Kit (JDK)
   b) Docker
   c) Microsoft SQL Server
   d) Kubernetes
3. Which of the following tools is used to manage Apache Spark installations?
   a) Spark Manager
   b) Hadoop Manager
   c) Conda
   d) Spark Package Manager (SPM)
4. What is the default file format for Spark configuration files?
   a) XML
   b) JSON
   c) YAML
   d) Properties
5. How can you verify the Spark installation?
   a) Running the command spark-submit --version
   b) Checking the Spark directory for logs
   c) Executing a MapReduce job
   d) Using a browser-based Spark simulator

Spark on Hadoop (YARN and HDFS)

6. What does YARN stand for in the Hadoop ecosystem?
   a) Yet Another Resource Negotiator
   b) Yarn Application Runtime Node
   c) Your Advanced Resource Network
   d) Yet Another Resource Namespace
7. Which mode allows Spark to run on Hadoop’s cluster manager?
   a) Standalone mode
   b) Client mode
   c) Cluster mode
   d) Mesos mode
8. What is HDFS used for in Spark?
   a) Managing SQL queries
   b) Storing and processing large datasets
   c) Visualizing Spark jobs
   d) Creating machine learning models
9. How does Spark interact with HDFS?
   a) Through REST APIs
   b) By accessing HDFS blocks directly
   c) Using JDBC connections
   d) By embedding HDFS in Spark applications
10. What is the benefit of running Spark on YARN?
    a) Improved monitoring
    b) Enhanced machine learning capabilities
    c) Resource sharing with other Hadoop services
    d) Pre-configured data transformations

Configuration and Environment Setup

11. Which file is used to define Spark-specific configurations?
    a) spark-env.sh
    b) spark-defaults.conf
    c) spark-config.yaml
    d) spark-setup.json
12. How can you configure Spark for high memory usage?
    a) Increase spark.executor.instances
    b) Modify spark.driver.memory and spark.executor.memory
    c) Change the number of partitions
    d) Adjust the shuffle buffer size
13. Which command starts the Spark standalone cluster?
    a) spark-cluster start
    b) start-master.sh
    c) spark-submit cluster
    d) start-spark.sh
14. What environment variable is essential for Spark to locate Hadoop?
    a) JAVA_HOME
    b) HADOOP_HOME
    c) SPARK_MASTER
    d) PYSPARK_HOME
15. How do you enable Spark logging?
    a) Edit the log4j.properties file
    b) Enable Spark UI monitoring
    c) Start the Spark shell with logging flags
    d) Use the Spark CLI

Introduction to Spark Shell (Scala, Python, R)

16. What is the command to launch the Spark Shell in Scala?
    a) spark-shell
    b) spark-scala
    c) launch-spark
    d) spark-init
17. Which language is used for PySpark?
    a) Python
    b) Scala
    c) R
    d) SQL
18. How can you execute a Spark application in the R language?
    a) Using SparkR
    b) Writing MapReduce code
    c) Deploying on a SQL engine
    d) By running shell scripts
19. What is the default port for accessing the Spark UI?
    a) 8080
    b) 4040
    c) 7070
    d) 9000
20. What is the purpose of the SparkContext in Spark Shell?
    a) Managing SQL queries
    b) Controlling the Spark application lifecycle
    c) Storing Spark logs
    d) Displaying real-time dashboards

Spark UI and Monitoring

21. What does the Spark UI provide?
    a) Real-time monitoring of Spark jobs
    b) Data visualization for business intelligence
    c) Machine learning model creation
    d) SQL query optimization
22. How can you access the Spark UI for a running application?
    a) Through a web browser
    b) By connecting to the database
    c) Using a local terminal command
    d) Through a pre-configured API
23. What does the “Stages” tab in the Spark UI display?
    a) Executor configurations
    b) Running and completed stages of a Spark job
    c) Job scheduling policies
    d) Resource allocation details
24. How can you monitor the memory usage of executors in Spark?
    a) Using the Spark UI “Executors” tab
    b) By running a shell script
    c) Through SQL queries
    d) By checking the Spark directory
25. Which metric is critical for identifying bottlenecks in Spark jobs?
    a) Disk I/O
    b) Shuffle read/write times
    c) Number of partitions
    d) Job duration

Additional Questions

26. What is the role of the Spark Driver?
    a) Managing and distributing tasks to executors
    b) Storing Spark datasets
    c) Scheduling Hadoop jobs
    d) Executing MapReduce tasks
27. Which mode is recommended for small-scale local Spark jobs?
    a) Standalone mode
    b) Cluster mode
    c) Client mode
    d) Embedded mode
28. What is the purpose of the spark-submit command?
    a) Submitting Spark applications to the cluster
    b) Configuring Spark environment variables
    c) Debugging Spark jobs
    d) Monitoring Spark logs
29. How is Spark’s resilience achieved during task failures?
    a) By replicating data across nodes
    b) Through task re-execution using RDD lineage
    c) By increasing executor memory
    d) Using machine learning models
30. What is the default cluster manager for Spark?
    a) YARN
    b) Mesos
    c) Standalone
    d) Kubernetes

Answers