50 Essential MCQs for Big Data Knowledge Assessment

Understanding Big Data

Big Data is a term used to describe the vast amounts of information that require a systematic approach to extract useful insights. This data is typically too complex to be processed by traditional data processing applications. Big Data has multiple characteristics that make it difficult to store, collect, maintain, analyze, and visualize.

Distributed File System

Distributed File System refers to a file system where data is stored on a server and accessed and processed as though it is on the local machine. It is transparent, simple to use, scalable, has high performance and reliability and offers high availability.

Big Data Characteristics

Big Data has three main characteristics which are volume, velocity, and variety. Volume refers to the scale of data, velocity pertains to the speed at which data is generated and variety refers to the different types of data available. There are four types of big data- structured, unstructured, semi-structured and hybrid.

Tools and Sources for Big Data

There are multiple tools and sources for big data including Apache Hadoop, Apache Storm, Cassandra, Mongo DB and sources like Amazon, Redshift and Mongo DB.

Challenges and Benefits of Big Data

There are several challenges associated with big data such as uncertainty of data management, talent gap in big data skills and synchronizing across data sources. There are also many benefits such as reduced cost, time savings, real-time data analysis, and faster decision-making.

Use Cases of Big Data

Big Data has numerous use cases including recommendation engines, call detail record analysis, fraud detection, market basket analysis, and sentiment analysis.

Big Data MCQs

There are multiple-choice questions associated with big data that help test and reinforce understanding of the concepts and technologies involved.

Explaining the Main Components of Big Data

// These are the main components of big data
HDFS // Hadoop Distributed File System for storage
MapReduce // programming model for processing big data
YARN // framework for job scheduling and cluster resource management 

All of these components are essential for the functioning of big data. HDFS is used for storing large amounts of data across various nodes in a distributed environment. MapReduce is a programming model that allows for parallel processing of data, making it easier to handle large amounts of data. YARN is a framework that manages job scheduling and resource management in a distributed environment. Together, these three components help to create a robust foundation for processing and managing big data.

Platforms for Hadoop

Hadoop is a cross-platform framework and can run on different operating systems, including Unix-like systems, Windows, and macOS. Therefore, option B is the correct answer.

// No code to optimize or rephrase

Understanding Big Data

Big data refers to extremely large sets of data that require advanced computing power to process and analyze. These datasets are typically measured in petabytes, which is equivalent to 1,000 terabytes, or 1 million gigabytes. Therefore, the correct answer is D) Data in petabyte size is known as big data.

// Example of declaring a variable representing big data size in bytes
long bigDataSizeInBytes = 1000000000000000L; // This is equal to 1 petabyte

Bank Data Transactions: Structured or Unstructured?

The correct answer is B) Structured data. Data transactions in banks typically involve organized and formatted data that follows a specific structure. This allows for easier storage, retrieval, and analysis of the data.

// Example of a structured bank transaction data
transaction = {
   "transaction_id": 123456789,
   "account_number": 987654321,
   "transaction_type": "deposit",
   "amount": 1000.00,
   "date": "2021-07-01"

In the example above, we can see that the transaction data is organized into specific fields and follows a pre-defined structure, making it easier to manipulate and analyze.

Understanding the Forms of Big Data

Big data can be categorized into three different forms:

Unstructured - data without a predefined model 
Structured - data with a predefined and organized model
Semi-Structured - data that contains both structured and unstructured data

Therefore, the correct answer is option C) - there are three forms of big data: unstructured, structured, and semi-structured.

Identifying Incorrect Big Data Technologies

As per my understanding, the following is the list of big data technologies, out of which one is incorrect:

  • Apache Kafka
  • Apache Hadoop
  • Apache Spark
  • Apache PyTorch

After analyzing all the options, I believe that the incorrect big data technology in the list is 'Apache PyTorch'.

Apache PyTorch

is not a big data technology; instead, it is an open source machine learning library based on the Torch library, which is primarily used for developing deep learning models.

Programming Language Used for Hadoop

Hadoop is written in Java. Therefore, it requires the installation of a Java Development Kit (JDK) on the machine where it will be used.

// Java code example
public class HadoopExample {
   public static void main(String[] args){
      System.out.println("Hadoop is written in Java.");

Understanding Big Data

Big data refers to a large collection of data that is constantly growing and used in significant volumes. This data comprises structured, semi-structured, and unstructured data that is generated from various sources.

The term "big" in big data refers to the size and complexity of the data rather than the physical size of the storage system. Big data requires high-performance computing systems to process, store, and analyze the data effectively.

Businesses use big data to gain insights into their operations and customer behavior patterns. Big data is also used in scientific research, healthcare, finance, and many other industries.

General-Purpose Computing for Distributed Data Analytics

The correct answer among the given options is B) MapReduce. MapReduce is a popular programming model that simplifies the process of processing large, distributed datasets across clusters of computers. It provides a runtime system that takes care of parallel processing, fault tolerance, and load balancing across multiple nodes. MapReduce is a general-purpose computing model that can be used for a variety of big data applications, including distributed data analytics.

//Sample code using MapReduce

// Mapper function for input data
map(key, value):
   // perform extraction and transformation
   emit(mapped_key, mapped_value)

// Reducer function for processing map output
reduce(key, values):
   // perform aggregation and calculation
   emit(reduced_key, reduced_value)

Primary Characteristics of Big Data

In order to understand big data, it is important to know its primary characteristics:

- Value
- Variety
- Volume

All of the above characteristics are equally important for big data.

Identifying Qubole as a Big Data Tool

The answer to the question is True. Qubole is a widely used big data platform that is utilized by various organizations for data processing and analytics. It provides an easy-to-use interface that simplifies the process of managing big data and enables the users to perform a wide range of data operations including processing, analyzing, and transforming data. Qubole supports multiple cloud-based platforms including AWS, Azure, Google Cloud, and Oracle Cloud, providing flexibility to users to choose the cloud provider of their choice.


//Example code of using Qubole on AWS

from qubole import Qubole
from qubole.commands import HiveCommand

# Authenticate your Qubole API key

# Submit an example Hive command
hc = HiveCommand.run(
    parameters=["region=us-west", "year=2009"])

Languages Used in Data Science

In data science, various programming languages are used for statistical analysis, data visualization, machine learning, and other tasks. Some of the commonly used languages include:


Therefore, the correct answer is C) R is used in data science.

The Data Science Process

In the data science process, the following steps are usually involved:

  1. Discovery
  2. Model Planning
  3. Operationalization
  4. Evaluation
  5. Deployment

Out of these options, communication building is not a part of the data science process.

Features of Big Data Analytics

Big Data Analytics has several features that make it popular. These features include:

  • Open-source: Big Data Analytics tools are usually open-source, which means they are freely available.
  • Data recovery: Big Data Analytics tools make data recovery easier by allowing companies to access and analyze large datasets quickly.
  • Scalability: Big Data Analytics tools can easily handle large amounts of data, making them scalable.

Therefore, the correct answer is D) All of the above.

// Example of using Big Data Analytics features in Python
import pandas as pd
import pyspark

# Open-source Big Data Analytics tool
# Accessing and analyzing large datasets
data = pd.read_csv('my_large_dataset.csv')

# Scalability with distributed computing
sc = pyspark.SparkContext('local[*]')
rdd = sc.parallelize(data)

Understanding the 5 V's of Big Data

In the world of big data, there are five critical factors that make up the 5 V's of big data:

  1. Volume: the amount of data being generated, stored, and analyzed.
  2. Velocity: the speed at which data is being generated and processed.
  3. Variety: the diversity of data being collected from various sources and in different formats.
  4. Veracity: the accuracy and reliability of the data being analyzed.
  5. Value: the insights and benefits that can be obtained from analyzing the data.

Therefore, the correct answer is C) There are a total of 5 V's of big data.

// Sample code to illustrate the concept of the 5 V's of Big Data
int volume = 1000; // represents the amount of data being generated and stored
double velocity = 150.5; // represents the speed at which data is being generated and processed
String[] variety = {"text", "images", "audio", "video"}; // represents the different types of data being collected
boolean veracity = true; // represents the accuracy and reliability of the data
String value = "Increased customer satisfaction"; // represents the insights and benefits obtained from analyzing the data

Reasons why Big Data Analysis is Difficult to Optimize

The correct reason why big data analysis is difficult to optimize is option B: Both data and cost-effective ways to mine data to make business sense out of it.

Big data sets are becoming increasingly complex and voluminous, making it difficult to extract meaningful insights from raw data. Additionally, the techniques and technologies required to effectively mine and analyze big data are constantly evolving, requiring significant investments in infrastructure and expertise. Businesses must also be able to balance the potential benefits of big data analysis with the costs of implementing and maintaining the necessary systems and processes.

In summary, optimizing big data analysis requires a combination of quality data and cost-effective techniques, which can be a challenging task for many organizations.

Description of Hadoop

Hadoop is a distributed computing framework that is open source and Java-based. It employs a distributed computing approach to process large data sets and helps in data-intensive computations. However, Hadoop is not a real-time platform.

Benefits of Big Data Processing

Big data processing offers several benefits to businesses, including:

  • The ability to utilize external intelligence when making decisions.
  • Improved operational efficiency.
  • Enhanced customer service.

Therefore, the correct answer is D) All of the above.

Big Data Analysis: What Does It Do?

Big Data analysis involves processing, organizing, and drawing insights from massive data sets. It helps in making informed decisions and improving business operations. However, it does not spread data or collect data; these are separate tasks that precede data analysis.

Therefore, the correct answer is: b) Big data analysis doesn't analyze data.

Understanding Big Data

The correct statement about big data is:

B) Big data refers to data sets that are at least a petabyte in size.

Traditional techniques are not enough to process big data as the size of such data makes it difficult for traditional data processing methods to handle and manage it effectively. The process of big data analysis involves reporting and data mining techniques, making option C incorrect. Additionally, big data has high velocity, which means that enormous amounts of data are generated at a remarkably fast rate, making option D incorrect.

Cleaning and Preparing Big Data

In general, a data warehouse is used to clean and prepare big data.

Other options listed:

  • Pandas: Pandas is a Python library primarily used for data manipulation and analysis. It is not specifically designed for cleaning and preparing big data, although it can be used for this purpose.
  • Data lake: A data lake is a system that stores unstructured and structured data in its native format. While a data lake can be used for analysis and preparing data, it is not specifically designed for cleaning and preparing data.
  • U-SQL: U-SQL is a query language used in Azure Data Lake Analytics for big data processing. Similar to a data lake, U-SQL can be used for preparing data, but it is not specifically designed for cleaning and preparing data.

Identifying Operations Performed in a Data Warehouse

In a data warehouse, various operations can be performed for data analysis and management. One of the commonly used operations is the "scan" operation. This operation involves reading data from the data warehouse and analyzing it for specific purposes.

Other operations that can be performed on a data warehouse include:

- Alter: changing the structure or design of the data warehouse - Modify: making changes to the data stored in the data warehouse - Read/write: accessing and editing the data in the data warehouse

It is essential to understand these operations to effectively manage and derive insights from the data stored in a data warehouse.

Component for ingesting streaming data into Hadoop

In Hadoop, the component responsible for ingesting streaming data is Flume.

  • Oozie: is a workflow scheduler system to manage Apache Hadoop jobs.
  • Hive: is a data warehouse software that helps in data analysis and SQL-based querying of data.
  • Kafka: is a distributed streaming platform that is used for building real-time data pipelines and streaming apps.
  • Flume: is used to stream log data into Hadoop clusters for efficient storage and analysis.


In the configuration file mapred-site.xml, the property to configure the host and port where MapReduce task runs is specified. Therefore, the correct answer is option D.

  <configuration> <br>

Identifying the Type of Mapper Class

The Mapper class is of generic type, denoted by the use of angle brackets<> in its declaration. This allows the class to work with different types of objects while maintaining compile-time type safety.

public class Mapper<T,E> { ... 

The class header specifies two type parameters T and E within angle brackets. These parameters can be replaced by any valid Java identifier to represent specific types when instantiating the class.

Job Control in Hadoop

In Hadoop, the control of jobs is managed by the Job class and not by Task, Mapper, or Reducer classes.

Identifying the Multidimensional Model of a Data Warehouse

In the context of data warehousing, the term "data cube" refers to the multidimensional model used to organize and represent the data. This model allows for the efficient retrieval and analysis of data from multiple dimensions, such as time, location, and product category. Unlike a traditional two-dimensional table structure, a data cube can store and retrieve vast amounts of data, making it an essential tool for data analysis and decision-making. Therefore, the correct answer to the given question is (B) Data cube.

Understanding MapReduce Job Splitting

In the context of MapReduce, the fixed-size pieces of a job are called "splits." Each split is a portion of the input data that the MapReduce job processes as a separate task using a map function. So, option A is the correct answer.

Here's an example of how splitting works:

// Pseudocode for splitting a MapReduce job
InputData = readInputData();
splits = splitInputData(InputData); // Split the input data into manageable pieces

for each split in splits do:
    scheduleTask(split);  // Schedule a task to process each split using a map function

Where is the Output of Map Tasks Written?

The output of map tasks is written on the local disk.

//example code
Mapper<LongWritable, Text, Text, IntWritable> mapper = new Mapper<LongWritable, Text, Text, IntWritable>(){
  public void map(LongWritable key, Text value, Context context){
    //map logic here
    context.write(new Text("output"), new IntWritable(1)); //write output to local disk

Time Horizon in Data Warehousing

In data warehousing, the time horizon refers to the length of time for which data is stored and available for analysis. The time horizon of a data warehouse typically ranges from 5 to 10 years.

Where Can Data be Updated?

Data updating can be performed in different environments which include:

  • Operational environment
  • Data warehouse environment
  • Data mining environment
  • Informational environment

Out of these options, the correct answer is: C) Operational environment.

An operational environment is where the data is created, updated, and deleted in real-time. It is vital for data analysts to be able to access up-to-date information to make informed decisions. Hence, the operational environment plays a crucial role in data updating.

// Here is a sample code illustrating data update operation in Java using JDBC connection


Hadoop Common Package is the core module of Hadoop that contains all the essential Java libraries and utilities required by other Hadoop modules. It includes jar files that are used by the Hadoop HDFS (Hadoop Distributed File System) and MapReduce. So, the correct option is D) jar files.

Understanding Data Warehouses: Data marts

In the context of data warehouses, small logical units that hold specific subsets of data and are designed for easy access and analysis are called data marts. They are created to serve the needs of specific business units or departments, and can be used to support decision-making, reporting, and analysis. Data marts are often used in combination with other data warehousing tools such as access layers, data storage, and data mining, to create a comprehensive and effective data solution. It is important for organizations to carefully plan and design their data warehousing solutions in order to ensure that they can effectively capture, store, and access their data.

Incorrect Data Warehouse Property

Volatile is not a characteristic of a data warehouse. However, the following are the three main properties of a data warehouse:

- Subject oriented - Time variant - Integration of data from various sources

The data is collected from heterogeneous sources and then transformed to make it easier to analyze. Additionally, data warehouses are designed to be subject-oriented, meaning that the data is organized around specific topics. Furthermore, data in a warehouse is organized over time to track changes and trends.

Identifying Slave Node

In a Hadoop cluster, the Data node is considered as the slave node. The other nodes in the cluster, such as the Job node, Task node, and Name node, are responsible for managing and orchestrating the Data node.

Therefore, the answer to the question "Identify the slave node among the following - Job node, Data node, Task node, Name node" is B) Data node.

// Sample code to demonstrate the role of the Data node in a Hadoop cluster
public class DataNode {
  // code for handling and storing the actual data

Identifying the Source of Data Warehouse Data

In a data warehouse, all data is collected from different sources and consolidated into a single repository for analysis and reporting. The environment where the data originates is known as the source environment.

Out of the options given, the correct answer is C) Operational environment. This is because data warehouse data is typically sourced from operational systems like CRMs, ERPs, and other transactional databases that store day-to-day business operations data.

The other options are incorrect because, although they may be related to the context of a data warehouse, they do not refer to the source of the data itself.

Fact Tables in Big Data

In the context of big data, fact tables are essential components that store quantitative data. They serve as a repository for measuring business processes, providing critical insights, and guiding decision-making processes.

To manage these fact tables, big data systems rely on several main components, including Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN). In combination, these systems provide a powerful framework for handling large-scale data processing tasks.

In short, the correct answer is D) All of the above, as each of these components plays a vital role in enabling the effective management and analysis of big data in modern data architectures.

Correctly Defining Reconciled Data

Reconciled data refers to the most recent and accurate information that a company uses as the primary source for its decision-making processes. It is the data that has undergone thorough and rigorous quality checks and has been verified as correct and complete.

This data is intended to be the single source of truth for all of a company's decision support systems, and it is usually stored in a central repository or data warehouse. The primary goal of reconciling data is to ensure that all stakeholders are using the same information, reducing errors and ambiguity in decision-making.

Identifying the Checkpoint Node in HDFS

In HDFS, the checkpoint node is responsible for checkpointing the namespace of the NameNode periodically. The checkpointing process helps in preventing metadata loss in case the NameNode fails.

The node that acts as a checkpoint node in the HDFS is the Secondary NameNode. It helps in performing the necessary backups of the namespace by continuously merging the edits with the fsimage to create a new checkpoint. However, it is essential to note that the Secondary NameNode is not a replacement for the real NameNode, which is responsible for maintaining the cluster's metadata.

Therefore, it is crucial to configure the checkpoint node correctly to ensure that it performs the backups efficiently.

Identifying Sources of Change Data in Refreshing a Data Warehouse

In accessing a data warehouse, the most common source of change data is Queryable change data. This type of change data allows for more timely and efficient updates to the warehouse by providing the ability to query only the data that has changed since the last refresh.

Logged change data is a type of change data that involves tracking all changes to the data over time. This approach can be resource-intensive and may not be necessary for all data warehouse applications.

Cooperative change data is a type of change data that involves multiple teams working together to track changes and ensure consistency across the data warehouse. This approach can be effective but requires a high degree of coordination and communication.

Snapshot change data involves capturing the state of the data at a particular point in time. This approach can be useful in certain situations, such as auditing, but is usually not the most efficient way to update a data warehouse on a regular basis.

In summary, Queryable change data is the most common and efficient source of change data for refreshing a data warehouse.

Understanding DSS in Data Warehousing

In data warehousing, DSS stands for "Decision Support System".

A DSS is a computer-based system that helps individuals and organizations make decisions by providing access to relevant data and analytical tools. This system is designed to support decision-making tasks that require extensive data analysis and modeling.

Other potential benefits of using a DSS include:

  • Improving data quality and consistency
  • Reducing the time and effort required for analysis
  • Enhancing collaboration and communication among decision-makers
  • Increasing overall organizational efficiency and effectiveness

Overall, a DSS can provide significant value to organizations seeking to improve their decision-making capabilities and optimize their use of data.

Definition of Metadata

Metadata refers to data that provides information about other data. This can include information about the content, formatting, structure, and other attributes of a particular dataset or file.

Approaches to integrating heterogeneous databases in data warehousing

In data warehousing, there are two approaches to integrating heterogeneous databases:

1. Query driver approach:

In this approach, the data is accessed by sending queries to the source databases and then integrating the results.

2. Update-driven approach:

This approach involves setting up triggers on the source databases to capture any changes made and then updating the data warehouse accordingly.

Therefore, the correct answer is A) There are two different approaches to integrating heterogeneous databases in data warehousing.

Factors Considered Before Investing in Data Mining

Before investing in data mining, there are several factors that are considered, which include:

  • Vendor Consideration: The reputation, reliability, and experience of the data mining vendor.
  • Functionality: The features and capabilities of the data mining software, including its ability to handle large datasets, analyze complex data, and provide accurate results.
  • Compatibility: The compatibility of the software with the existing systems and data architecture of the organization.

Therefore, option D is the correct answer as all of the above factors are considered before investing in data mining.

Classification of "Efficiency and Scalability of Data Mining Algorithms" Issues

The issues related to the efficiency and scalability of data mining algorithms fall under the category of performance issues.

Performance issues are concerned with the speed and scalability of the algorithms used in data mining, as well as their ability to handle large and complex datasets.

Other issues pertaining to data mining methodologies and user interaction, as well as diverse data types, are categorized separately.

System of Data Warehousing for Reporting and Data Analysis

Out of the options provided, the system of data warehousing is mostly used for reporting and data analysis. This means that data is collected from various sources, stored in a centralized location (data warehouse), and then analyzed to provide insights into business performance, trends, and other key metrics.

Data mining and data storage refers to the process of discovering patterns from large datasets, which can be useful in a variety of applications such as fraud detection or market research. Data integration and data storage is the process of combining data from different sources and loading it into a centralized location. Data cleaning and data storage involves the process of cleaning up data before it is stored in a data warehouse.

Overall, data warehousing is a critical component of many businesses, as it allows them to make informed decisions based on the analysis of large datasets.

Understanding the Use of Data Cleaning

Data cleaning is the process of identifying and fixing erroneous, incomplete, or irrelevant data in a dataset. It plays a crucial role in data mining, as it ensures data accuracy, consistency, and reliability. There are several uses of data cleaning:

  • To remove noisy data and improve data quality
  • To transform data and correct wrong or incomplete data entries
  • To correct inconsistencies in data, such as misspelled names, abbreviations, or formatting errors

In summary, data cleaning helps to enhance the value of data by ensuring that it is accurate, consistent, and free from errors that could lead to biased or incorrect results. Therefore, all of the above are valid uses of data cleaning.


There is no code required for this concept.

Minimum Data Size in HDFS

In HDFS, the minimum amount of data that can be read or written by a disk is known as the block size. Therefore, the answer to the given question is (B) Block size.

Code: N/A

Note: This question does not require any code implementation.

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