Apache Spark MCQ

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Apache Spark, a lightning-fast, unified analytics engine, has established itself as a major player in the big data ecosystem. Known for large-scale data processing, Spark offers modules for structured data processing, machine learning, graph computation, and more. Whether you’re just beginning your Spark journey or looking to refresh the basics, this set of MCQs is tailor-made for you. Dive in and test your knowledge!

1. Apache Spark is primarily written in which language?

a) Java
b) Python
c) Scala
d) Go

Answer:

c) Scala

Explanation:

Apache Spark is mainly written in Scala, but it provides APIs for Java, Scala, Python, and R.

2. Which Spark module provides a programming interface for data structured in rows and columns?

a) Spark Streaming
b) Spark SQL
c) Spark MLlib
d) GraphX

Answer:

b) Spark SQL

Explanation:

Spark SQL offers a programming interface for structured data and allows querying the data using SQL.

3. Which of the following is NOT a core component of Spark?

a) Driver Program
b) Cluster Manager
c) Executors
d) Zookeeper

Answer:

d) Zookeeper

Explanation:

Zookeeper is not a core component of Spark. It is primarily used in the Hadoop ecosystem.

4. Which data structure represents an immutable, distributed collection of objects in Spark?

a) DataFrame
b) DataSet
c) RDD (Resilient Distributed Dataset)
d) Block

Answer:

c) RDD (Resilient Distributed Dataset)

Explanation:

RDD is the fundamental data structure in Spark representing an immutable, distributed collection of objects.

5. In which mode does Spark run if you don’t configure a Cluster Manager?

a) YARN
b) Mesos
c) Standalone
d) Kubernetes

Answer:

c) Standalone

Explanation:

By default, if no Cluster Manager is specified, Spark runs in Standalone mode.

6. Which Spark library allows real-time data processing?

a) Spark MLlib
b) Spark SQL
c) GraphX
d) Spark Streaming

Answer:

d) Spark Streaming

Explanation:

Spark Streaming is designed for real-time data processing and analysis.

7. What command in the Spark shell is used to stop the SparkContext?

a) spark.stop()
b) stop.spark()
c) spark.exit()
d) exit.spark()

Answer:

a) spark.stop()

Explanation:

To stop the SparkContext, the spark.stop() command is used in the Spark shell.

8. Which function is used to transform one RDD into another RDD in Spark?

a) map()
b) reduce()
c) groupBy()
d) filter()

Answer:

a) map()

Explanation:

The map() function is used to transform the data in one RDD to create a new RDD.

9. In Spark, partitions are…

a) Logical chunks of data
b) Physical storage spaces
c) Nodes in the cluster
d) Separate clusters

Answer:

a) Logical chunks of data

Explanation:

In Spark, partitions represent logical chunks of data, allowing for distributed data processing.

10. Spark's MLlib is used for…

a) Graph computation
b) Real-time processing
c) Machine Learning
d) SQL-based querying

Answer:

c) Machine Learning

Explanation:

MLlib is Spark’s machine learning library, providing several algorithms and utilities for ML tasks.

11. What is the role of the Spark Driver?

a) To run the main function and create RDDs.
b) To physically store data.
c) To distribute data across cluster nodes.
d) To manage network traffic.

Answer:

a) To run the main function and create RDDs.

Explanation:

The Spark Driver runs the main application, creates RDDs, and schedules tasks on the executors.

12. How can you cache an RDD in Spark?

a) rdd.cacheMe()
b) rdd.store()
c) rdd.keep()
d) rdd.cache()

Answer:

d) rdd.cache()

Explanation:

The rdd.cache() method is used in Spark to cache an RDD for faster access during repeated operations.

13. Which Spark component communicates with the cluster manager to ask for resources?

a) Executors
b) SparkContext
c) Driver Program
d) Tasks

Answer:

b) SparkContext

Explanation:

SparkContext is responsible for communicating with the cluster manager and coordinating the allocation of resources.

14. Spark supports which of the following file formats for data processing?

a) JSON, Parquet, and Avro
b) XML only
c) Text files only
d) CSV only

Answer:

a) JSON, Parquet, and Avro

Explanation:

Apache Spark supports various file formats, including JSON, Parquet, and Avro, among others.

15. DataFrames in Spark are similar to tables in…

a) Word documents
b) RDBMS
c) PowerPoint
d) Paint

Answer:

b) RDBMS

Explanation:

DataFrames in Spark can be considered equivalent to tables in Relational Database Management Systems (RDBMS) with support for querying using SQL.

16. For handling large graphs and graph computation, Spark provides…

a) GraphFrame
b) GraphSQL
c) GraphDB
d) GraphX

Answer:

d) GraphX

Explanation:

GraphX is Spark's API for graphs and graph computation.

17. The primary programming abstraction of Spark Streaming is…

a) Continuous Data Stream
b) DStream
c) FastStream
d) RStream

Answer:

b) DStream

Explanation:

DStream, or Discretized Stream, is the primary abstraction in Spark Streaming representing a continuous stream of data.

18. Which of the following can be a source of data for Spark Streaming?

a) Kafka
b) HBase
c) MongoDB
d) SQLite

Answer:

a) Kafka

Explanation:

Kafka is a popular source for Spark Streaming, allowing for the processing of real-time data streams.

19. How can Spark be integrated with Hadoop?

a) By using Spark with HDFS for storage.
b) By replacing Hadoop's MapReduce with Spark.
c) Both a and b.
d) None of the above.

Answer:

c) Both a and b.

Explanation:

Spark can use HDFS for storage and can also replace Hadoop's MapReduce for processing, giving more flexibility and performance.

20. What is the advantage of using DataFrames or Datasets over RDDs?

a) They are more resilient.
b) They allow for low-level transformations.
c) They provide optimizations using Catalyst and Tungsten.
d) They are more challenging to use.

Answer:

c) They provide optimizations using Catalyst and Tungsten.

Explanation:

DataFrames and Datasets benefit from Spark's Catalyst optimizer and Tungsten execution engine for performance improvements.

21. What does the 'reduceByKey' function do in Spark?

a) Reduces the dataset size by a factor specified by the key.
b) Groups the dataset based on keys.
c) Merges the values for each key using an associative reduce function.
d) Filters out all entries that don't match the specified key.

Answer:

c) Merges the values for each key using an associative reduce function.

Explanation:

The reduceByKey function in Spark merges the values for each key using the given reduce function.

22. In Spark's local mode, how many worker nodes does it run on?

a) Multiple nodes as specified.
b) Zero nodes.
c) Only one node.
d) Depends on the cluster manager.

Answer:

c) Only one node.

Explanation:

In local mode, Spark runs on a single machine with one executor.


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