PySpark Multiple-Choice Questions (MCQs)

PySpark is the Python API for Apache Spark, an open source, distributed computing framework and set of libraries for real-time, large-scale data processing.

PySpark MCQs: This section contains multiple-choice questions and answers on the various topics of PySpark. Practice these MCQs to test and enhance your skills on PySpark.

List of PySpark MCQs

1. An API for using Spark in ____ is PySpark.

  1. Java
  2. C
  3. C++
  4. Python

Answer: D) Python

Explanation:

An API for using Spark in Python is PySpark.

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2. Using Spark, users can implement big data solutions in an ____-source, cluster computing environment.

  1. Closed
  2. Open
  3. Hybrid
  4. None

Answer: B) Open

Explanation:

Using Spark, users can implement big data solutions in an open-source, cluster computing environment.

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3. In PySpark, ____ library is provided, which makes integrating Python with Apache Spark easy.

  1. Py5j
  2. Py4j
  3. Py3j
  4. Py2j

Answer: B) Py4j

Explanation:

In PySpark, Py4j library is provided, which makes integrating Python with Apache Spark easy.

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4. Which of the following is/are the feature(s) of PySpark?

  1. Lazy Evaluation
  2. Fault Tolerant
  3. Persistence
  4. All of the above

Answer: D) All of the above

Explanation:

The following are the features of PySpark -

  1. Lazy Evaluation
  2. Fault Tolerant
  3. Persistence

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5. In-memory processing of large data makes PySpark ideal for ____ computation.

  1. Virtual
  2. Real-time
  3. Static
  4. Dynamic

Answer: B) Real-time

Explanation:

In-memory processing of large data makes PySpark ideal for real-time computation.

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6. A variety of programming languages can be used with the PySpark framework, such as ____, and R.

  1. Scala
  2. Java
  3. Python
  4. All of the above

Answer: D) All of the above

Explanation:

A variety of programming languages can be used with PySpark framework, such as Scala, Java, Python, and R.

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7. In memory, PySpark processes data 100 times faster, and on disk, the speed is __ times faster.

  1. 10
  2. 100
  3. 1000
  4. 10000

Answer: B) 100

Explanation:

In memory, PySpark processes data 100 times faster, and on disk, the speed is 10 times faster.

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8. When working with ____, Python's dynamic typing comes in handy.

  1. RDD
  2. RCD
  3. RBD
  4. RAD

Answer: A) RDD

Explanation:

When working with RDD, Python's dynamic typing comes in handy.

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9. The Apache Software Foundation introduced Apache Spark, an open-source ____ framework.

  1. Clustering Calculative
  2. Clustering Computing
  3. Clustering Concise
  4. Clustering Collective

Answer: B) Clustering Computing

Explanation:

The Apache Software Foundation introduced Apache Spark, an open-source clustering computing framework.

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10. ____ are among the key features of Apache Spark. It is easy to use, provides simplicity, and can run virtually anywhere.

  1. Stream Analysis
  2. High Speed
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

Stream analysis and high speed are among the key features of Apache Spark. It is easy to use, provides simplicity, and can run virtually anywhere.

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11. The Apache Spark framework can perform a variety of tasks, such as ____, running Machine Learning algorithms, or working with graphs or streams.

  1. Executing distributed SQL
  2. Creating data pipelines
  3. Inputting data into databases
  4. All of the above

Answer: D) All of the above

Explanation:

The Apache Spark framework can perform a variety of tasks, such as executing distributed SQL, creating data pipelines, inputting data into databases, running Machine Learning algorithms, or working with graphs or streams.

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12. Programming in ____ is the official language of Apache Spark.

  1. Scala
  2. PySpark
  3. Spark
  4. None

Answer: A) Scala

Explanation:

Programming in Scala is the official language of Apache Spark.

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13. Scala is a ____ typed language as opposed to Python, which is an interpreted, ____ programming language.

  1. Statically, Dynamic
  2. Dynamic, Statically
  3. Dynamic, Partially Statically
  4. Statically, Partially Dynamic

Answer: A) Statically, Dynamic

Explanation:

Scala is a statically typed language as opposed to Python, which is an interpreted, dynamic programming language.

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14. A ____ program is written in Object-Oriented Programming (OOP).

  1. Python
  2. Scala
  3. Both A and B
  4. None of the above

Answer: A) Python

Explanation:

A Python program is written in Object-Oriented Programming (OOP).

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15. ____ must be specified in Scala.

  1. Objects
  2. Variables
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

Objects and variables must be specified in Scala.

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16. Python is __ times slower than Scala.

  1. 2
  2. 5
  3. 10
  4. 20

Answer: C) 10

Explanation:

Python is 10 times slower than Scala.

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17. As part of Netflix's real-time processing, ____ is used to make an online movie or web series more personalized for customers based on their interests.

  1. Scala
  2. Dynamic
  3. Apache Spark
  4. None

Answer: C) Apache Spark

Explanation:

As part of Netflix's real-time processing, Apache Spark is used to make an online movie or web series more personalized for customers based on their interests.

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18. Targeted advertising is used by top e-commerce sites like ____, among others.

  1. Flipkart
  2. Amazon
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

Targeted advertising is used by top e-commerce sites like Flipkart and Amazon, among others.

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19. Java version 1.8.0 or higher is required for PySpark, as is ____ version 3.6 or higher.

  1. Scala
  2. Python
  3. C
  4. C++

Answer: B) Python

Explanation:

Java version 1.8.0 or higher is required for PySpark, as is Python version 3.6 or higher.

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20. Using Spark____, we can set some parameters and configurations to run a Spark application on a local cluster or dataset.

  1. Cong
  2. Conf
  3. Con
  4. Cont

Answer: B) Conf

Explanation:

Using SparkConf, we can set some parameters and configurations to run a Spark application on a local cluster or dataset.

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21. Which of the following is/are the feature(s) of the SparkConf?

  1. set (key, value)
  2. setMastervalue (value)
  3. setAppName (value)
  4. All of the above

Answer: D) All of the above

Explanation:

The following are the features of the SparkConf -

  1. set (key, value)
  2. setMastervalue (value)
  3. setAppName (value)

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22. Spark programs initially create a Spark____ object to instruct them how to access the cluster.

  1. Contact
  2. Context
  3. Content
  4. Config

Answer: B) Context

Explanation:

Spark programs initially create a SparkContext object to instruct them how to access the cluster.

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23. Pyspark provides SparkContext by default as __.

  1. sc
  2. st
  3. sp
  4. se

Answer: A) sc

Explanation:

Pyspark provides SparkContext by default as sc.

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24. Which of the following parameter(s) is/are accepted by SparkContext?

  1. Master
  2. appName
  3. SparkHome
  4. All of the above

Answer: D) All of the above

Explanation:

The following parameters are accepted by SparkContext -

  1. Master
  2. appName
  3. SparkHome

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25. The Master ___ identifies the cluster connected to Spark.

  1. URL
  2. Site
  3. Page
  4. Browser

Answer: A) URL

Explanation:

The Master URL identifies the cluster connected to Spark.

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26. The ____ directory contains the Spark installation files.

  1. SparkHome
  2. pyFiles
  3. BatchSize
  4. Conf

Answer: A) SparkHome

Explanation:

The SparkHome directory contains the Spark installation files.

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27. The PYTHONPATH is set by sending ____ files to the cluster.

  1. .zip
  2. .py
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

The PYTHONPATH is set by sending .zip or .py files to the cluster.

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28. This number corresponds to the BatchSize of the Python ____.

  1. Objects
  2. Arrays
  3. Stacks
  4. Queues

Answer: A) Objects

Explanation:

This number corresponds to the BatchSize of the Python objects.

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29. The batching can be disabled by setting it to ____.

  1. 0
  2. 1
  3. Void
  4. Null

Answer: B) 1

Explanation:

The batching can be disabled by setting it to 1

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30. An integrated ____ programming API is provided by PySpark SQL in Spark.

  1. Relational-to-functional
  2. Functional-to-functional
  3. Functional-to-relational
  4. None of the above

Answer: A) Relational-to-functional

Explanation:

An integrated relational-to-functional programming API is provided by PySpark SQL in Spark.

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31. What is/are the drawback(s) of Hive?

  1. In other words, if the workflow execution fails in the middle, you cannot recover the position from which it stopped.
  2. Changing the trash setting will prevent us from dropping encrypted databases in cascade.
  3. MapReduce executes ad-hoc queries, which are launched by Hive, but the performance of the analysis is delayed due to the medium-sized database.
  4. All of the above

Answer: D) All of the above

Explanation:

The drawbacks of Hive are -

  1. In other words, if the workflow execution fails in the middle, you cannot recover the position from which it stopped.
  2. Changing the trash setting will prevent us from dropping encrypted databases in cascade.
  3. MapReduce executes ad-hoc queries, which are launched by Hive, but the performance of the analysis is delayed due to the medium-sized database.

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32. What is/are the feature(s) of PySpark SQL?

  1. Consistence Data Access
  2. Incorporation with Spark
  3. Standard Connectivity
  4. All of the above

Answer: D) All of the above

Explanation:

The features of PySpark SQL are -

  1. Consistence Data Access
  2. Incorporation with Spark
  3. Standard Connectivity

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33. The Consistent Data Access feature allows SQL to access a variety of data sources, such as ____, JSON, and JDBC, from a single place.

  1. Hive
  2. Avro
  3. Parquet
  4. All of the above

Answer: D) All of the above

Explanation:

The Consistent Data Access feature allows SQL to access a variety of data sources, such as Hive, Avro, Parquet, JSON, and JDBC, from a single place.

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34. For business intelligence tools, the industry standard is ____ connectivity, which are both used for standard connectivity.

  1. JDBC
  2. ODBC
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

For business intelligence tools, the industry standard is JDBC or ODBC connectivity, which are both used for standard connectivity.

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35. What is the full form of UDF?

  1. User-Defined Formula
  2. User-Defined Functions
  3. User-Defined Fidelity
  4. User-Defined Fortray

Answer: B) User-Defined Functions

Explanation:

The full form of UDF is User-Defined Functions.

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36. A UDF extends Spark SQL's DSL vocabulary for transforming DataFrames by defining a new ____-based function.

  1. Row
  2. Column
  3. Tuple
  4. None

Answer: B) Column

Explanation:

A UDF extends Spark SQL's DSL vocabulary for transforming DataFrames by defining a new column-based function.

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37. Spark SQL and DataFrames include the following class(es):

  1. pyspark.sql.SparkSession
  2. pyspark.sql.DataFrame
  3. pyspark.sql.Column
  4. All of the above

Answer: D) All of the above

Explanation:

Spark SQL and DataFrames include the following classes:

  1. pyspark.sql.SparkSession
  2. pyspark.sql.DataFrame
  3. pyspark.sql.Column

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38. DataFrame and SQL functionality is accessed through ____.

  1. pyspark.sql.SparkSession
  2. pyspark.sql.DataFrame
  3. pyspark.sql.Column
  4. pyspark.sql.Row

Answer: A) pyspark.sql.SparkSession

Explanation:

DataFrame and SQL functionality are accessed through pyspark.sql.SparkSession.

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39. ____ represents a set of named columns and distributed data.

  1. pyspark.sql.GroupedData
  2. pyspark.sql.DataFrame
  3. pyspark.sql.Column
  4. pyspark.sql.Row

Answer: B) pyspark.sql.DataFrame

Explanation:

pyspark.SQL.DataFrame represents a set of named columns and distributed data.

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40. ____ returns aggregation methods.

  1. DataFrame.groupedBy()
  2. Data.groupBy()
  3. Data.groupedBy()
  4. DataFrame.groupBy()

Answer: D) DataFrame.groupBy()

Explanation:

DataFrame.groupBy() returns aggregation methods.

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41. Missing data can be handled via ____.

  1. pyspark.sql.DataFrameNaFunctions
  2. pyspark.sql.Column
  3. pyspark.sql.Row
  4. pyspark.sql.functions

Answer: A) pyspark.sql.DataFrameNaFunctions

Explanation:

Missing data can be handled via pyspark.sql.DataFrameNaFunctions.

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42. A list of built-in functions for DataFrame is stored in ____.

  1. pyspark.sql.functions
  2. pyspark.sql.types
  3. pyspark.sql.Window
  4. All of the above

Answer: A) pyspark.sql.functions

Explanation:

A list of built-in functions for DataFrame is stored in pyspark.sql.functions.

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43. ____ in PySpark UDF are similar to their functions in Pandas.

  1. map()
  2. apply()
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

map() and apply() in PySpark UDF are similar to their functions in Pandas.

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44. Which of the following is/are the common UDF problem(s)?

  1. Py4JJavaError
  2. Slowness
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

The following are the common UDF problems -

  1. Py4JJavaError
  2. Slowness

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45. What is the full form of RDD?

  1. Resilient Distributed Dataset
  2. Resilient Distributed Database
  3. Resilient Defined Dataset
  4. Resilient Defined Database

Answer: A) Resilient Distributed Dataset

Explanation:

The full form of RDD is Resilient Distributed Dataset.

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46. In terms of schema-less data structures, RDDs are one of the most fundamental, as they can handle both ____ information.

  1. Structured
  2. Unstructured
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

In terms of schema-less data structures, RDDs are one of the most fundamental, as they can handle both structured and unstructured information.

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47. A ____ memory abstraction, resilient distributed datasets (RDDs), allows programmers to run in-memory computations on clustered systems.

  1. Compressed
  2. Distributed
  3. Concentrated
  4. Configured

Answer: B) Distributed

Explanation:

A distributed memory abstraction, resilient distributed datasets (RDDs), allows programmers to run in-memory computations on clustered systems.

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48. The main advantage of RDD is that it is fault ____, which means that if there is a failure, it automatically recovers.

  1. Tolerant
  2. Intolerant
  3. Manageable
  4. None

Answer: A) Tolerant

Explanation:

The main advantage of RDD is that it is fault-tolerant, which means that if there is a failure, it automatically recovers.

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49. The following type(s) of shared variable(s) are supported by Apache Spark -

  1. Broadcast
  2. Accumulator
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

The following types of shared variables are supported by Apache Spark -

  1. Broadcast
  2. Accumulator

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50. Rather than shipping a copy of a variable with each task, broadcast lets the programmer store a ____-only variable locally.

  1. Read
  2. Write
  3. Add
  4. Update

Answer: A) Read

Explanation:

Rather than shipping a copy of a variable with each task, broadcast lets the programmer store a read-only variable locally.

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51. ___ operations are carried out on the accumulator variables to combine the information.

  1. Associative
  2. Commutative
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

Associative and commutative operations are carried out on the accumulator variables to combine the information.

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52. Using ____, PySpark allows you to upload your files.

  1. sc.updateFile
  2. sc.deleteFile
  3. sc.addFile
  4. sc.newFile

Answer: C) sc.addFile

Explanation:

Using sc.addFile, PySpark allows you to upload your files.

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53. With ____, we can obtain the working directory path.

  1. SparkFiles.get
  2. SparkFiles.fetch
  3. SparkFiles.set
  4. SparkFiles.go

Answer: A) SparkFiles.get

Explanation:

With SparkFiles.get, we can obtain the working directory path.

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54. To decide how RDDs are stored, PySpark has different StorageLevels, such as the following:

  1. DISK_ONLY
  2. DISK_ONLY_2
  3. MEMORY_AND_DISK
  4. All of the above

Answer: D) All of the above

Explanation:

To decide how RDDs are stored, PySpark has different StorageLevels, such as the following:

  1. DISK_ONLY
  2. DISK_ONLY_2
  3. MEMORY_AND_DISK

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55. Among the method(s) that need to be defined by the custom profiler is/are:.

  1. Profile
  2. Stats
  3. Add
  4. All of the above

Answer: D) All of the above

Explanation:

Among the methods that need to be defined by the custom profiler are:

  1. Profile
  2. Stats
  3. Add

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56. class pyspark.BasicProfiler(ctx) implements ____ as a default profiler.

  1. cProfile
  2. Accumulator
  3. Both A and B
  4. None of the above

Answer: C) Both A and B

Explanation:

class pyspark.BasicProfiler(ctx) implements cProfile and Accumulator as a default profiler.

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57. Job and stage progress can be monitored using PySpark's ___-level APIs.

  1. Low
  2. High
  3. Average
  4. None

Answer: A) Low

Explanation:

Job and stage progress can be monitored using PySpark's low-level APIs.

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58. The active stage ids are returned by ____ in an array.

  1. getActiveStageIds()
  2. getJobIdsForGroup(jobGroup = None)
  3. getJobInfo(jobId)
  4. All of the above

Answer: A) getActiveStageIds()

Explanation:

The active stage ids are returned by getActiveStageIds() in an array.

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59. A tuning procedure on Apache Spark is performed using PySpark ____.

  1. SparkFiles
  2. StorageLevel
  3. Profiler
  4. Serialization

Answer: D) Serialization

Explanation:

A tuning procedure on Apache Spark is performed using PySpark Serialization.

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60. Serializing another function can be done using the ____ function.

  1. map()
  2. data()
  3. get()
  4. set()

Answer: A) map()

Explanation:

Serializing another function can be done using the map() function.

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