20 Data Warehousing MCQs with Answers

Data Warehousing is a valuable skill for many data-related roles like Data Engineering. Industries implement data warehousing to store large amounts of data that can later be used for making informed decisions. A well-designed data warehouse helps tech professionals to access it efficiently.

Proficiency in this area is crucial for building efficient data pipelines and ensuring data integrity. By engaging with these MCQs on data warehousing, Data Engineers, Data Analysts, and Business Analysts can reinforce their understanding of the core concepts.

You must revisit such interview questions while you are on a self-learning journey. These are the first steps to test your knowledge on fundamentals. Once you gauge your performance on these basic concepts, you can proceed with more advanced questions.

MCQs are the first step toward your extensive interview preparation. We have more crucial and advanced questions you can explore to test your knowledge.

We have curated a list of MCQs on data warehousing. These questions address the integration of data warehousing with BI tools for data mining and forecasting, data transformation processes, the central role of the data warehouse database server, and the stages of ETL processes. 

Also Read: Data Engineer Career Path to Follow in 2024

Interview Questions on Data Warehousing 

Dividing deep into data warehousing, we will cover different types of MCQs, including BI tools, ETL MCQs, data engineering interview questions, and data warehousing MCQs. 

Data warehousing is the process involving the collection, storage, and management of data for the organizational benefit. 

Also Read: How to Prepare for Data Engineer Interviews

Q1. What is the combination of data warehousing and BI tools used for:

1. Data mining 
2. Forecasting 
3. Decrease data organization 
4. Both a and b 

Answer: d. Both a and b 

Q2. Which of the following defines data transformation

1. Merging data from two different sources
2. Merging data from two similar sources 
3. Changing data from summary to detailed level 
4. Converting data from detailed to summary level 

Answer: d. Converting data from detailed to summary level 

Q3. Which is considered the heart of the data warehouse: 

1. Relational database server 
2. Data Mart database server 
3. Data warehouse database server
4. All of the above 

Answer: c. Data warehouse database server

Q4. Where are different data stages used and verified during ETL

1. Destination 
2. Source 
3. Only by administrator  
4. Both a and b  

Answer: d. Both a and b

Q5. Reading from the database is synonymous with which process

1. Extraction 
2. Transformation 
3. Loading 
4. All of the above 

Answer: a. Extraction 

Q6. How many types of transformations are in ETL

1. 1
2. 2
3. 3
4. 4

Answer: 2 

Q7. What is the importance of lookup transformation

1. Update of slowly modifying dimension table 
2. Obtaining the desired value from the table through the column value 
3. Verification of the prior existence of a record in the table
4. All of the above 

Answer: d. All of the above 

Q8. Which of these options correctly describes reconciled data

1. Data storage in one operational system 
2. Data storage in different operational systems 
3. Current data is intended to be a single source for all decision support systems 
4. Data chosen for end-user support application 

Answer: a. Data storage in one operational system 

Q9. What do you mean by OLAP

1. Online Analytical Performance 
2. Online Advanced Processing 
3. Online Analytical Processing 
4. Online Advanced Preparation

Answer:  c. Online Analytical Processing 

Q10. On which of these factors do OLTP and OLAP differ? 

1. Database size 
2. Complexity of queries 
3. Types of business tasks 
4. All of the above 

Answer: d. All of the above 

Q11.  Which of the following best describes real-time data warehousing?

1. A process that extracts, transforms, and loads data from various sources into a centralized repository for analysis and reporting in near real-time
2. The practice of storing historical data in a data warehouse for long-term analysis and decision-making
3. A method of data integration that involves periodic batch updates to the data warehouse
4. An approach where data is stored in separate silos, with no centralized repository for analysis

Answer: A process that extracts, transforms, and loads data from various sources into a centralized repository for analysis and reporting in near real-time

Q12. Which of these tests will ensure regional suitability (including language and culture) of a software application for a global audience

1. Regression testing 
2. Usability testing 
3. Localization testing 
4. Compatibility testing 

Answer: c. Localization testing 

Q13. Which architecture is suited for analytical processing and complex queries on large datasets? 

1. ETL 
2. CRM
3. OLTP
4. OLAP

Answer: d. OLAP 

Q14. What are the components of metadata

1. Data structure 
2. Summarization algorithm 
3. Mapping connecting the data warehouse with the operational environment 
4. All of the above 

Answer: d. All of the above 

Q15. Which approach is used by the optimizer during the execution plan

1. Rule based
2. Cost based 
3. Both a and b
4. None of the above 

Answer: c. Both a and b

Q16. Which of these is the main function of SCD or the Slowly Changing Dimension in a data warehouse? 

1. Facilitating data migration 
2. Maintaining historical data over time 
3. Enhancing data visualization 
4. Improving database performance 

Answer: b. Maintaining historical data over time 

Q17. Which of the following best defines "time horizon" in the context of a data warehouse?

1. The duration between data refresh cycles in the data warehouse
2. The range of time covered by the historical data stored in the data warehouse
3. The time taken to process and analyze data within the data warehouse
4. The duration for which real-time data is stored in the data warehouse.

Answer: b. The range of time covered by the historical data stored in the data warehouse

Q18. What is the time horizon in the data warehouse

1. 1 to 2 years 
2. 1 to 2 months 
3. 5 to 10 years 
4. 5 to 10 months 

Answer: c. 5 to 10 years 

Q19. Which option erases and reloads the tables with new information

1. Full refresh 
2. Initial load 
3. Incremental load 
4. Both b and c 

Answer: a. Full refresh 

Q20. What is the significance of ETL for businesses

1. Analysis of business data 
2. Repository of data 
3. Facilitation of data relocation 
4. All of the above 

Answer: d. All of the above

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FAQs: Data Warehousing MCQs

Q1. Is Databricks a data warehouse? 

No, Databricks is not a data warehouse but a data analytics platform. 

Q2. What are the benefits of data warehousing?

Data warehousing offers multiple benefits, such as saving time, storing historical data, increasing data security, improving business intelligence, leading to data consistency, and others. 

Q3. Is SQL considered ETL?

SQL or Structured Query Language is not considered ETL or Extract, Transform, and Load. Yet, it plays a significant role in the process. SQL is one among multiple components of the broad ETL process. 

Q4. What are the three steps in building a data warehouse? 

The three fundamental steps in building a data warehouse are requirement analysis and planning, data modeling and design, and ETL development and implementation. 

Q5. Do all companies have a data warehouse? 

No, not all companies have a data warehouse. However, proper data handling is needed at every business, regardless of its scale. 

Q6. What is the data warehouse lifecycle? 

The data warehouse lifecycle includes the following components: Data modeling, ETL design and development, OLAP cubes, UI development, maintenance, test and deployment, and requirement specification. 

Q7. What are the three data warehouse models? 

The three data warehouse models are enterprise warehouse, data mart, and virtual warehouse. 

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