Top 70 PySpark Multiple Choice Questions (MCQs) with Answers

1 .

An API for using Spark in ____ is PySpark.

A)

C

B)

PHP

C)

Python

D)

Java

Correct Answer : Python

Explanation : An API for using Spark in Python is PySpark.

2 .

Which of the following statements is true about PySpark?

A)

PySpark is a standalone data processing system.

B)

PySpark does not support distributed processing.

C)

PySpark is used for processing data only in small batches.

D)

PySpark is a Python library used for Big Data processing.

Correct Answer : PySpark is a Python library used for Big Data processing.

Explanation : PySpark is a Python library used for Big Data processing. It is built on top of Apache Spark, which is a distributed computing system. PySpark provides APIs in Python for data processing, machine learning, and graph processing.

3 .

Which of the following is a transformation operation in PySpark?

A)

filter()

B)

count()

C)

collect()

D)

reduce()

Correct Answer : filter()

Explanation : filter() is a transformation operation in PySpark. It creates a new RDD by selecting elements from an existing RDD based on a condition. Other transformation operations in PySpark include map(), flatMap(), union(), distinct(), and groupByKey().

4 .

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

A)

Closed

B)

Open

C)

Hybrid

D)

None of the above

Correct Answer : Open

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

5 .

Which of the following libraries lets PySpark communicate with Spark's Scala API?

A)

PyJ

B)

PyS4J

C)

Py4j

D)

PySpark4J

Correct Answer : Py4j

6 .

Serializing another function can be done using the ____ function.

A)

set()

B)

map()

C)

get()

D)

data()

Correct Answer : map()

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

7 .

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

A)

Serialization

B)

Profiler

C)

SparkFiles

D)

StorageLevel

Correct Answer : Serialization

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

8 .

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

A)

getJobInfo(jobId)

B)

getActiveStageIds()

C)

getJobIdsForGroup(jobGroup = None)

D)

All of the above

Correct Answer : getActiveStageIds()

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

9 .

Which of the following is an action operation in PySpark?

A)

map()

B)

filter()

C)

flatMap()

D)

count()

Correct Answer : count()

Explanation : count() is an action operation in PySpark. It returns the number of elements in an RDD. Other action operations in PySpark include collect(), reduce(), take(), and foreach().

10 .

Which of the following is used to create an RDD in PySpark?

A)

DataSet

B)

DataFrame

C)

SparkContext

D)

SQLContext

Correct Answer : SparkContext

Explanation : SparkContext is used to create an RDD in PySpark. It is the entry point to the Spark computing system and provides APIs to create RDDs, accumulates values, and manipulate data. Other Spark components in PySpark include SQLContext, SparkSession, and DataFrameReader.

11 .

Which of the following statements are true about PySpark SparkContext?

A)

It sets up internal services

B)

It sends the application to executors

C)

It does not execute tasks in each executor

D)

Establishes a connection to an execution environment

Correct Answer : It sets up internal services

12 .

What is the entry point of the PySpark program that allows connecting to the Spark cluster?

A)

SparkContent

B)

SparkContext

C)

ContentSpark

D)

ContextSpark

Correct Answer : SparkContext

13 .

Which of the following is true about PySpark DataFrame?

A)

They are mutable in nature

B)

They are distributed in nature

C)

Execution starts before an action is triggered

D)

None of the above

Correct Answer : They are distributed in nature

14 .

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

A)

Low

B)

High

C)

Average

D)

None of the above

Correct Answer : Low

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

15 .

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

A)

Add

B)

Stats

C)

Profile

D)

All of the above

Correct Answer : All of the above

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

* Add
* Stats
* Profile

16 .

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

A)

cProfile

B)

Accumulator

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

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

17 .

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

A)

Persistence

B)

Fault Tolerant

C)

Lazy Evaluation

D)

All of the above

Correct Answer : All of the above

Explanation : The following are the features of PySpark :

* Lazy Evaluation
* Fault Tolerant
* Persistence

18 .

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

A)

Static

B)

Real-time

C)

Virtual

D)

Dynamic

Correct Answer : Real-time

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

19 .

Which of the following is used to read data from a file in PySpark?

A)

readTextFile()

B)

writeTextFile()

C)

readDataFrame()

D)

writeDataFrame()

Correct Answer : readTextFile()

Explanation : readTextFile() is used to read data from a file in PySpark. It reads the contents of a file and creates an RDD with each line of the file as an element. Other file input/output operations in PySpark include read.csv(), read.json(), write.csv(), and write.json().

20 .

Which of the following is used to convert an RDD to a DataFrame in PySpark?

A)

fromRDD()

B)

RDDtoDF()

C)

toDataFrame()

D)

createDataFrame()

Correct Answer : createDataFrame()

Explanation : createDataFrame() is used to convert an RDD to a DataFrame in PySpark. It creates a DataFrame from an RDD with a specified schema. Other DataFrame operations in PySpark include select(), filter(), groupBy(), and join().

21 .

Which of the following is used to cache an RDD in memory in PySpark?

A)

persist()

B)

cache()

C)

collect()

D)

saveAsTextFile()

Correct Answer : persist()

Explanation : persist() is used to cache an RDD in memory in PySpark. It stores the RDD in memory and/or on disk so that it can be reused efficiently in subsequent operations. Other RDD operations in PySpark include mapPartitions(), sortByKey(), reduceByKey(), and aggregateByKey().

22 .

With ____, we can obtain the working directory path.

A)

SparkFiles.go

B)

SparkFiles.get

C)

SparkFiles.set

D)

SparkFiles.fetch

Correct Answer : SparkFiles.get

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

23 .

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

A)

DISK_ONLY

B)

DISK_ONLY_2

C)

MEMORY_AND_DISK

D)

All of the above

Correct Answer : All of the above

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

* DISK_ONLY
* DISK_ONLY_2
* MEMORY_AND_DISK

24 .

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

A)

Java

B)

Scala

C)

Python

D)

All of the above

Correct Answer : All of the above

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

25 .

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

A)

RCD

B)

RAD

C)

RDD

D)

RBD

Correct Answer : RDD

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

26 .

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

A)

10

B)

100

C)

1000

D)

10000

Correct Answer : 10

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

27 .

Which of the following is a transformation operation that shuffles data in PySpark?

A)

groupByKey()

B)

map()

C)

filter()

D)

reduce()

Correct Answer : groupByKey()

Explanation : groupByKey() is a transformation operation that shuffles data in PySpark. It groups the values of each key in an RDD and creates a new RDD of (key, value) pairs. Other shuffling operations in PySpark include sortByKey(), reduceByKey(), and aggregateByKey().

28 .

Which of the following is used to create a PairRDD in PySpark?

A)

zip()

B)

map()

C)

flatMap()

D)

groupByKey()

Correct Answer : zip()

Explanation : zip() is used to create a PairRDD in PySpark. It creates a new RDD by aggregating the elements of two RDDs into pairs. The first element of each RDD becomes the key, and the second element becomes the value. Other PairRDD operations in PySpark include reduceByKey(), groupByKey(), and join().

29 .

Which of the following is used to broadcast a read-only variable in PySpark?

A)

broadcast()

B)

rdd.broadcast()

C)

spark.broadcast()

D)

sc.broadcast()

Correct Answer : sc.broadcast()

Explanation : sc.broadcast() is used to broadcast a read-only variable in PySpark. It broadcasts the variable to all nodes in a Spark cluster so that it can be accessed efficiently by tasks. Other broadcasting operations in PySpark include accumulators and counters.

30 .

Which of the following option helps to process data in HiveQL?

A)

RDD

B)

Dataframe

C)

PySpark Core

D)

PySpark SQL

Correct Answer : PySpark SQL

31 .

Which of the following option is used to create RDD?

A)

Using join() function

B)

Using filter() function

C)

Using parallel() function

D)

Using parallelize() function

Correct Answer : Using parallelize() function

32 .

Which of the following commands helps to submit PySpark code to cluster?

A)

spark-cluster

B)

spark cluster

C)

spark-submit

D)

spark submit

Correct Answer : spark-submit

33 .

Which of the following options help to see RDD contents?

A)

RDDread.Collect()

B)

RDDread.Content()

C)

RDD.CollectContent()

D)

RDDread.ContentCollect()

Correct Answer : RDDread.Collect()

34 .

Which of the following is a built-in machine learning algorithm in PySpark?

A)

Random Forest

B)

Linear Regression

C)

K-Means Clustering

D)

All of the above

Correct Answer : All of the above

Explanation : PySpark provides several built-in machine learning algorithms, including Linear Regression, K-Means Clustering, Random Forest, Decision Trees, Gradient Boosting, and Naive Bayes. These algorithms can be used for regression, classification, clustering, and collaborative filtering.

35 .

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

A)

Clustering Concise

B)

Clustering Computing

C)

Clustering Collective

D)

Clustering Calculative

Correct Answer : Clustering Computing

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

36 .

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

A)

High Speed

B)

Stream Analysis

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

Explaination : 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.

37 .

Programming in ____ is the official language of Apache Spark.

A)

Scala

B)

Spark

C)

PySpark

D)

None of the above

Correct Answer : Scala

Explanation : Programming in Scala is the official language of Apache Spark.

38 .

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

A)

Statically, Dynamic

B)

Dynamic, Statically

C)

Statically, Partially Dynamic

D)

Dynamic, Partially Statically

Correct Answer : Statically, Dynamic

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

39 .

Python is __ times slower than Scala.

A)

2

B)

5

C)

10

D)

20

Correct Answer : 10

Explanation : Python is 10 times slower than Scala.

40 .

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

A)

C

B)

C++

C)

Scala

D)

Python

Correct Answer : Python

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

41 .

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

A)

Associative

B)

Commutative

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

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

42 .

Using ____, PySpark allows you to upload your files.

A)

sc.newFile

B)

sc.addFile

C)

sc.deleteFile

D)

sc.updateFile

Correct Answer : sc.addFile

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

43 .

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

A)

Broadcast

B)

Accumulator

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

Explanation : The following types of shared variables are supported by Apache Spark :

* Broadcast
* Accumulator

44 .

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

A)

set (key, value)

B)

setAppName (value)

C)

setMastervalue (value)

D)

All of the above

Correct Answer : All of the above

Explanation : The following are the features of the SparkConf :

* set (key, value)
* setMastervalue (value)
* setAppName (value)

45 .

The Master ___ identifies the cluster connected to Spark.

A)

URL

B)

Site

C)

Page

D)

Browser

Correct Answer : URL

Explanation : The Master URL identifies the cluster connected to Spark.

46 .

The ____ directory contains the Spark installation files.

A)

Conf

B)

pyFiles

C)

BatchSize

D)

SparkHome

Correct Answer : SparkHome

Explanation : The SparkHome directory contains the Spark installation files.

47 .

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

A)

.py

B)

.zip

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

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

48 .

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

A)

Configured

B)

Distributed

C)

Compressed

D)

Concentrated

Correct Answer : Distributed

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

49 .

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

A)

Tolerant

B)

Intolerant

C)

Manageable

D)

None of the above

Correct Answer : Tolerant

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

50 .

What is the full form of RDD?

A)

Resilient Defined Dataset

B)

Resilient Distributed Dataset

C)

Resilient Defined Database

D)

Resilient Distributed Database

Correct Answer : Resilient Distributed Dataset

Explanation : The full form of RDD is Resilient Distributed Dataset.

51 .

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

A)

Slowness

B)

Py4JJavaError

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

Explanation : The following are the common UDF problems :

* Py4JJavaError
* Slowness

52 .

This number corresponds to the BatchSize of the Python ____.

A)

Stacks

B)

Arrays

C)

Queues

D)

Objects

Correct Answer : Objects

Explanation : This number corresponds to the BatchSize of the Python objects.

53 .

The batching can be disabled by setting it to ____.

A)

0

B)

1

C)

Void

D)

Null

Correct Answer : 1

Explanation : The batching can be disabled by setting it to 1

54 .

Which of the following is used to aggregate data in PySpark?

A)

aggregate()

B)

reduce()

C)

collect()

D)

groupByKey()

Correct Answer : aggregate()

Explanation : aggregate() is used to aggregate data in PySpark. It applies a function to each partition of an RDD and then combines the results using another function. Other aggregation operations in PySpark include reduce(), fold(), and combineByKey().

55 .

Which of the following is used to sort data in PySpark?

A)

sort()

B)

groupByKey()

C)

reduceByKey()

D)

sortByKey()

Correct Answer : sortByKey()

Explanation : sortByKey() is used to sort data in PySpark. It sorts an RDD of (key, value) pairs by the key in ascending or descending order. Other sorting operations in PySpark include sort(), sortBy(), and sortByValue().

56 .

Which of the following is used to write data to a file in PySpark?

A)

readTextFile()

B)

readDataFrame()

C)

writeTextFile()

D)

writeDataFrame()

Correct Answer : writeTextFile()

Explanation : writeTextFile() is used to write data to a file in PySpark. It writes the contents of an RDD to a file with each element of the RDD on a separate line. Other file input/output operations in PySpark include write.csv(), write.json().

57 .

Which of the following is used to read data from a CSV file in PySpark?

A)

readCSV()

B)

readJSON()

C)

readTextFile()

D)

read.parquet()

Correct Answer : readCSV()

Explanation : readCSV() is used to read data from a CSV file in PySpark. It reads the contents of a CSV file and creates a DataFrame with each row of the file as a separate row in the DataFrame. Other file input/output operations in PySpark include read.json(), read.parquet(), and read.text().

58 .

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

A)

Functional-to-relational

B)

Relational-to-functional

C)

Functional-to-functional

D)

None of the above

Correct Answer : Relational-to-functional

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

59 .

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

A)

Changing the trash setting will prevent us from dropping encrypted databases in cascade.

B)

In other words, if the workflow execution fails in the middle, you cannot recover the position from which it stopped.

C)

MapReduce executes ad-hoc queries, which are launched by Hive, but the performance of the analysis is delayed due to the medium-sized database.

D)

All of the above

Correct Answer : All of the above

Explanation : The drawbacks of Hive are :

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

60 .

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

A)

pyspark.sql.types

B)

pyspark.sql.Window

C)

pyspark.sql.functions

D)

All of the above

Correct Answer : pyspark.sql.functions

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

61 .

Missing data can be handled via ____.

A)

pyspark.sql.Row

B)

pyspark.sql.Column

C)

pyspark.sql.functions

D)

pyspark.sql.DataFrameNaFunctions

Correct Answer : pyspark.sql.DataFrameNaFunctions

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

62 .

____ returns aggregation methods.

A)

Data.groupBy()

B)

DataFrame.groupBy()

C)

Data.groupedBy()

D)

DataFrame.groupedBy()

Correct Answer : DataFrame.groupBy()

Explanation : DataFrame.groupBy() returns aggregation methods.

63 .

Which of the following is used to rename a column in a PySpark DataFrame?

A)

rename()

B)

column()

C)

withColumn()

D)

renameColumn()

Correct Answer : rename()

Explanation : rename() is used to rename a column in a PySpark DataFrame. It renames the specified column to a new name. Other DataFrame operations in PySpark include withColumn(), withColumnRenamed(), and drop().

64 .

Which of the following is used to write data to a Parquet file in PySpark?

A)

write.csv()

B)

write.parquet()

C)

write.json()

D)

write.text()

Correct Answer : write.parquet()

Explanation : write.parquet() is used to write data to a Parquet file in PySpark. Parquet is a columnar storage format that is optimized for query performance. Other file input/output operations in PySpark include write.csv(), write.json(), and write.text().

65 .

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

A)

Standard Connectivity

B)

Consistence Data Access

C)

Incorporation with Spark

D)

All of the above

Correct Answer : All of the above

Explanation : The features of PySpark SQL are :

* Standard Connectivity
* Consistence Data Access
* Incorporation with Spark

66 .

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

A)

Hive

B)

Avro

C)

Parquet

D)

All of the above

Correct Answer : All of the above

Explaination : 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.

67 .

What is the full form of UDF?

A)

User-Defined Fidelity

B)

User-Defined Fortray

C)

User-Defined Functions

D)

User-Defined Formula

Correct Answer : User-Defined Functions

Explanation : The full form of UDF is User-Defined Functions.

68 .

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

A)

Tuple

B)

Row

C)

Column

D)

None of the above

Correct Answer : Column

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

69 .

DataFrame and SQL functionality is accessed through ____.

A)

pyspark.sql.Row

B)

pyspark.sql.Column

C)

pyspark.sql.DataFrame

D)

pyspark.sql.SparkSession

Correct Answer : pyspark.sql.SparkSession

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

70 .

____ represents a set of named columns and distributed data.

A)

pyspark.sql.DataFrame

B)

pyspark.sql.Row

C)

pyspark.sql.Column

D)

pyspark.sql.GroupedData

Correct Answer : pyspark.sql.DataFrame

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