Apache Spark Multiple Choice Questions - Part 5

Objective

This Apache Spark Quiz is designed to test your Spark knowledge. It contains frequently asked Spark multiple choice questions along with the detailed explanation of their answers. These Spark quiz questions cover all the basic components of Spark ecosystem. Hope this objective type questions on Spark will help you to Spark interview preparation.

Best Apache Spark Multiple Choice Questions and Answers

Q.1 We can use Spark over Hadoop in-

Standalone

YARN

SIMR (Spark in MapReduce)

All of the above

For detailed description follow: Spark – Hadoop Compatibility

Q.2 Which of the following is module for Structured data processing?

GraphX

MLlib

Spark SQL

Spark R

For detailed description follow: Spark – Ecosystem Components

Q.3 Which of the following is fundamental datastructure of Spark?

RDD

DataFrame

Dataset

None of the above

For detailed description follow: Spark – RDD

Q.4 DataFrame in Apache Spark prevails over RDD and doesnot contain any feature of RDD.

TRUE

FALSE

For detailed description follow: Spark SQL – DataFrame

Q.5 Which of the following are common feature of RDD and DataFrame?

immutability

in-memory

resilient

All of the above

For detailed description follow: RDD vs DataFrame

Q.6 Which of the following is not true for DataFrame?

DataFrame in Apache Spark is behind RDD

We can build DataFrame from different data sources. structured data file, tables in Hive

The Application Programming Interface (APIs) of DataFrame is available in various languages.

Both in Scala and Java, we represent DataFrame as Dataset of rows.

For detailed description follow: Spark SQL – DataFrame

Q.7 We can create DataFrame using-

Tables in Hive

Structured data files

External databases

All of the above

For detailed description follow: Spark SQL – DataFrame

Q.8 RDD allows Java serialization.

TRUE

FALSE

For detailed description follow: Spark – RDD

Q.9 Which of the following is not true for Apache Spark Execution?

To simplify working with structured data it provides DataFrame abstraction in Python, Java, and Scala.

The data can be read and written in a variety of structured formats. For example, JSON, Hive Tables, and Parquet.

Using SQL we can query data,only from inside a Spark program and not from external tools.

The best way to use Spark SQL is inside a Spark application. This empowers us to load data and query it with SQL.

For detailed description follow: Apache Spark working

Q.10 If we launch the application through Spark submit, application jar doesnot gets distributed to all worker nodes.

TRUE

FALSE

For detailed description follow: Apache Spark working

Q.11 When SQL run from the other programming language the result will be-

DataFrame

DataSet

Either DataFrame or Dataset

Neither DataFrame nor Dataset

For detailed description follow: Spark SQL – Introduction Tutorial

Q.12 Which of the following are uses of Apache Spark SQL?

It executes SQL queries.

We can read data from existing Hive installation using SparkSQL.

When we run SQL within another programming language we will get the result as Dataset/DataFrame.

All of the above

For detailed description follow: Spark SQL – Introduction Tutorial

Q.13 With the help of Spark SQL, we can query structured data as a distributed dataset (RDD).

TRUE

FALSE

Aapche Spark Interview Questions and Answers

For detailed description follow: Spark SQL – Introduction Tutorial

Q.14 Which of the following is true for Spark SQL?

Hive transactions are not supported by Spark SQL.

No support for time-stamp in Avro table.

Even if the inserted value exceeds the size limit, no error will occur.

All of the above

For detailed description follow: Spark SQL – Introduction Tutorial

Q.15 Which of the followimg is true for Catalyst optimizer?

The optimizer helps us to run queries much faster than their counter RDD part.

The optimizer helps us to run queries little faster than their counter RDD part.

The optimizer helps us to run queries in the same speed as their counter RDD part.

None of the above

For detailed description follow: Spark SQL – Optimization

Q.16 The Dataset API is accessible in-

Java and Scala

Java, Scala and python

Scala and Python

Scala and R

For detailed description follow: Spark DataSet Tutorial

Q.17 Does Dataset API support Python and R.

yes

For detailed description follow: Spark DataSet Tutorial

Q.18 which of the following provide object-oriented programming interface?

RDD

DataFrame

Dataset

All of the above

For detailed description follow: Spark DataSet Tutorial

Q.19 Which of the following the data is organised into named coloumn?

RDD

DataFrame

Dataset

a and b

For detailed description follow: Spark SQL DataFrame

Q.20 Spark SQL can connect through JDBC or ODBC.

TRUE

FALSE

For detailed description follow: Spark SQL – Introduction Tutorial

Are you preparing for Spark developer job??? Refer these top 50+ Apache Spark Interview Questions and Answers for the best Spark interview preparation.

See Also-

3 thoughts on “Apache Spark Multiple Choice Questions – Part 5”

Reply ↓

Deepak Madan September 5, 2017 at 11:40 am

answer of question 17 should be No. While clicking on NO, its displaying Wrong !!!
- Reply ↓
  
  Pranab Datta May 28, 2018 at 8:06 am
  
  You are right Deepak. For Question 17 correct answer will be No.
Reply ↓

Sunil R September 30, 2017 at 2:00 pm

Answer for 19th question is option b and c and its not a and b. Please confirm

Learn Today. Lead Tomorrow.

Learn Today. Lead Tomorrow.

Apache Spark Multiple Choice Questions – Part 5 3

Q.1 We can use Spark over Hadoop in-

Q.2 Which of the following is module for Structured data processing?

Q.3 Which of the following is fundamental datastructure of Spark?

Q.4 DataFrame in Apache Spark prevails over RDD and doesnot contain any feature of RDD.

Q.5 Which of the following are common feature of RDD and DataFrame?

Q.6 Which of the following is not true for DataFrame?

Q.7 We can create DataFrame using-

Q.8 RDD allows Java serialization.

Q.9 Which of the following is not true for Apache Spark Execution?

Q.10 If we launch the application through Spark submit, application jar doesnot gets distributed to all worker nodes.

Q.11 When SQL run from the other programming language the result will be-

Q.12 Which of the following are uses of Apache Spark SQL?

Q.13 With the help of Spark SQL, we can query structured data as a distributed dataset (RDD).

Q.14 Which of the following is true for Spark SQL?

Q.15 Which of the followimg is true for Catalyst optimizer?

Q.16 The Dataset API is accessible in-

Q.17 Does Dataset API support Python and R.

Q.18 which of the following provide object-oriented programming interface?

Q.19 Which of the following the data is organised into named coloumn?

Q.20 Spark SQL can connect through JDBC or ODBC.

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3 thoughts on “Apache Spark Multiple Choice Questions – Part 5”