Data Warehouse MCQ Questions and Answers

By BYJU'S Exam Prep

Updated on: September 25th, 2023

Data Warehouse MCQ Questions and Answers and detailed solutions are available here. Data Warehouse MCQ Questions and Answers will provide help in preparing for the upcoming CSE Engineering exam. We have provided 5 MCQ-based questions here.

Check out the detailed solution for the Data Warehouse MCQ Questions and Answers, as it will help you clear blockage in solving similar types of questions. Objective-based MCQ and answers will help in the complete study of the data warehouse topics.

Data Warehouse MCQ Question 1

The process of removing deficiencies and loopholes in the data is called ____.

  1. Data aggregation
  2. Extraction of data
  3. Compression of data
  4. Cleaning of data

Answer: D. Cleaning of data


Data cleaning is preparing data for analysis by eliminating or changing data that is wrong, incomplete, irrelevant, or duplicated. These data types are typically not helpful when it comes to data analysis because they may impede the process or produce false results.

The process of getting information out of a database is called data extraction. The first step in an ETL process for ingesting data is data extraction.

Data aggregation is the process of gathering data and expressing it in a condensed form.

The process of altering, encoding, or transforming data’s bit structure so that it takes up less space on a disk is known as data compression.

Therefore option D is correct.

Data Warehouse MCQ Question 2

Which of the following retail analytic applications involve(s) the use of search techniques to gain insights into customer’s buying patterns?

  1. Factor analysis
  2. Regression analysis
  3. Data mining
  4. Data scrapping

Answer: D. Data scrapping


  • Data mining is a computer science concept, but it has had a big impact on the retail sector since it enables businesses to understand the patterns and habits of their customers’ purchasing. The information that retailers continue to gather includes transactional data, demographic data, and sales of seasonal products. Data mining is one of the most crucial techniques for extracting meaningful information from the vast amount of information gathered over time. Additionally, it is used to boost revenue production and lower operating expenses. Therefore, data mining techniques are used in retail analytic applications to get insights into customers’ buying patterns.
  • Factor analysis is a method for condensing many variables into a small number of factors. This method creates a common score by combining all variables’ maximum common variance. That score can be utilized for additional analysis as an index of all the variables.
  • Data scraping is a process where a computer programme pulls data from another program’s output that can be read by humans. Binary data, display formatting, superfluous labels, and other information that is either unnecessary or impediments to automated processing are frequently ignored. When there are no other means for data transmission available, it is typically seen as an ad hoc, the inelegant technique employed as a last resort.
  • Regression analysis: Finding the variables that have an effect on an interesting issue can be done with accuracy using regression analysis. You can confidently establish which elements are most important, which ones can be ignored, and how these factors interact when you do a regression.

Data Warehouse MCQ Question 3

An Enterprise Resource Planning application is an example of a(n) ______.

  1. Single-user database application
  2. Multiuser database application
  3. E-commerce database application
  4. Data mining database application

Answer: B. Multiuser database application


Enterprise Resource Planning (ERP): Businesses use this type of software to manage routine business operations like accounting, project management, and supply chain management. Supports in budget planning, financial forecasting, and reporting for an organization. An illustration of a multiuser database application is this.

What does the following statement represent in a data migration? Locate the useful information required to populate the customer relationship management and identify who maintains it, what it contains and how accurate it is.

Data Warehouse MCQ Question 4

  1. Identify available migration tools
  2. Test before migrating
  3. Improve the data
  4. Find the data

Answer: D. Find the data


Data migration is one of the most recent technological advancements. Businesses need technology to move data from one location to another, from one application to another, or from one format to another. The most important component of every organization is its data, and businesses frequently need to shift this data. In such a circumstance, data migration comes to the rescue. Data migration is also carried out through these programs, even though cloud-based applications currently house the majority of information. Finding the data, processing the data, and loading the data are the typical phases of data migration. The following types of data migration are possible:

  • Storage Migration
  • Cloud Migration
  • Application Migration

Data Warehouse MCQ Question 5

What do data warehouses support?

  1. OLAP
  3. OLTP
  4. Operational database

Answer: A. OLAP


Typically, historical data derived from transactional data is found in data warehouses. ETL (extraction, transformation, and loading), an OLAP engine, and other tools that manage data gathering and delivery to business customers are all included in a data warehouse. An OLAP system is an example of a data warehouse.

The online analytical processing (OLAP) system aids in data analysis. It enables analysts to quickly respond to queries that call for information from many source systems.      

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