Top 23 Database Schema Design Interview Questions

Got a job interview coming up? If you know you’ll be talking about database design, make sure you can answer these questions on database modeling and schemas.

Job interviews typically cover core knowledge areas, but the interviewer also often throws in some questions on tricky topics. We’ll help you prepare by going over these common database schema design questions that cover both basic and more advanced topics.

Basic Database and Data Modeling Questions

The following database schema design interview questions can be considered basic or beginner level. You should be able to answer them fully or at least partially to demonstrate that you are knowledgeable on the topic. We’ve also provided some examples of answers to those questions, but more details would help you stand out.

1. What is a data model?

Answer: A data model is a way of organizing and storing data in a database. It determines how data is organized and structured and how it relates to one another. Some common database models include the relational model, the object-oriented model, and the hierarchical model.

2. What are the three types of data models for relational database design?

Answer: There are three main types of data models that are commonly used when designing a relational database:

• Conceptual data model: This is a high-level model that represents the overall structure of the data, including the entities and the relationships between them. It is used to communicate the overall design of the database to stakeholders and does not include implementation details.
• Logical data model: This is a more detailed model that represents the structure of the data as it will be stored in the database. It includes more implementation details and specifies the attributes and data types for each entity.
• Physical data model: This is the most detailed model; it represents how the data will be physically stored on the database server. It includes information about the specific database management system (DBMS) that will be used, the storage structures, and the indexes and other performance-enhancing features that will be implemented.

These three data models are typically developed in a top-down fashion, starting with the conceptual model and then progressing to the logical and physical models as the design becomes more refined.

3. What is a database diagram?

Answer: A database diagram is a visual representation of a database schema. It shows the relationships between different entities (tables or other structures) in the database, and how the data is organized and related.

A database diagram can be used to understand the overall structure of a database and how the different entities are related to each other. It can also be used to design and plan the structure of a database, as well as to communicate the design to others.

There are several types of database diagrams. These include entity-relationship diagrams (ER diagrams), which show the relationships between entities, and object-oriented diagrams, which show the relationships between objects in an object-oriented database.

4. What are some best practices when designing a database?

Answer: There are several best practices that are generally recommended when designing a database:

• Use a clear and consistent naming convention: It is important to have a clear and consistent naming convention for database objects, such as tables and columns. This helps to make the database more understandable and easier to use.
• Normalize the data: Normalization is the process of organizing the data in a database to minimize redundancy and dependency. Normalized databases are generally more efficient and easier to maintain.
• Use appropriate data types: It is important to use appropriate data types for each column in a table. Using the correct data type helps to ensure that the data is stored and processed efficiently and accurately.
• Define primary and foreign keys: A primary key is a column that uniquely identifies each row in a table (e.g. a student ID number, a driver’s license number, etc.) A foreign key refers to another table’s primary key and establishes the relationship between two tables. It is important to define these keys carefully to ensure that the relationships between tables are correctly enforced.
• Use indexes to improve performance: Indexes improve the performance of database queries by allowing the database server to quickly locate the data it needs. However, it is important to use indexes wisely, as too many indexes can negatively impact performance.
• Test and optimize the database design: It is important to test the database design and optimize it as needed to ensure that it performs well and meets the needs of the application. This may involve fine-tuning queries, adding indexes, or making other changes to the database structure.

5. What is normalization?

Answer: Normalization is the process of organizing the data in a database to minimize redundancy and dependency. It involves breaking down data into smaller, more atomic pieces and organizing them into separate tables. The goal of normalization is to create a more efficient and flexible database design that is easier to maintain and modify over time.

6. What is denormalization and what is its purpose?

Answer: Denormalization is a process of intentionally adding redundancy to a database design in order to improve performance. It is used to reduce the number of joins required to retrieve data and improve query performance.

Denormalization is often used in read-heavy environments, such as data warehousing or OLTP systems, but can make the database more complex and prone to inconsistencies. It should be carefully considered and only used when the benefits outweigh the potential drawbacks.

7. What is a surrogate key and how is it different from a primary key?

Answer: A surrogate key is a unique identifier that is artificially generated by the database and used to uniquely identify each row in a table. It is often used in place of a natural primary key, which is a column or set of columns that naturally and uniquely identifies each row in the table.

Surrogate keys are often used when a natural primary key does not exist, is too long or complex, or may change over time. They provide a stable and efficient means of identifying rows in the table and establishing relationships between tables.

8. How can you enforce that only data that is valid from a business point of view is inserted in database tables and/or columns?

Answer: There are several ways to enforce the insertion of valid data only; the most common is to use constraints. Constraints are rules that are applied to a database table or column to enforce data integrity. They can be used to specify the types of data that are allowed in a given column as well as to enforce other rules like uniqueness or foreign key relationships. Some examples of constraints that can be used to enforce data integrity include NOT NULL, UNIQUE, PRIMARY KEY/FOREIGN KEY, and CHECK constraints.

9. What is cardinality in data modeling?

Answer: In data modeling, cardinality refers to the relationship between two entities in a database. It can be one of three types: one-to-one, one-to-many, or many-to-many. The type and direction of the cardinality determines how many records in one table (or entity) can be associated with a record in the other table. Cardinality is important in defining the structure and relationships between entities in a database.

10. Give me some examples of index types and briefly explain how they impact  performance and how they work in relation to the data model.

Answer: There are several types of indexes that can be used to improve the performance of a database, including clustered, non-clustered, and full-text indexes.

Clustered indexes physically rearrange the data in the table to match the index order, while non-clustered indexes create a separate data structure with index key values and pointers to the rows in the table.

Full-text indexes are used to search large amounts of text data. Indexes can be created on specific columns or combinations of columns in a table and can improve the performance of queries by allowing the database to quickly locate the requested data. However, it's important to carefully consider which indexes to create, as adding too many indexes can negatively impact the performance of insert, update, and delete operations.

11. What is a schema in a database?

Answer: A schema in a database is a logical structure that represents the organization of data in a database. It defines the tables, columns, and relationships between the data, as well as the data types and other constraints for each column.

A schema can be thought of as a blueprint for the database, providing a clear and organized structure for storing and accessing data. Schema design is an important aspect of database development, as it affects the performance, security, and overall functionality of the database.

12. What is the difference between OLTP and OLAP?

Answer: OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) are two different types of database systems that are optimized for different types of tasks.

OLTP systems are designed to handle a large number of short, transactional queries quickly and efficiently. These systems are used to store and retrieve data for day-to-day business operations, such as processing transactions or updating customer information.

OLAP systems, on the other hand, are designed to handle complex, analytical queries that involve large amounts of data. These systems are used to analyze and summarize data for business intelligence and decision-making purposes, such as identifying trends or forecasting future performance.

In general, OLTP systems are optimized for inserting, updating, and deleting small amounts of data quickly; OLAP systems are optimized for querying large amounts of data for analysis.

13. What are fact and dimension tables?

Answer: Fact tables contain measurements or metrics that are tracked, while dimension tables contain descriptive attributes or characteristics of the data. Fact and dimension tables are used together in a data warehouse to provide insights and context for the data.

14. What is a composite key in a data model?

Answer: A composite key is also known as a compound key or a concatenated key. It is a combination of multiple attributes that can together uniquely identify a row in a table. For example, the combination of first name, last name, and birth date could be used as a composite key; neither of these three attributes could uniquely identify a person, but together they could. Thus, composite keys are often used in data models when there is no single attribute that could uniquely identify a record. They can also be used to enforce relationships between tables in a database. For example, a composite key could be used to link a fact table to a dimension table in a data warehouse.

15. What is an ERD?

Answer: An entity-relationship diagram (ERD) is a graphical representation of the relationships between entities in a database. It is used to design and model databases and to communicate the structure of a database to stakeholders. ERDs use symbols and notation to represent the entities, attributes, and relationships in the database.

16. Give an example of a database antipattern.

Answer: One example of a database antipattern is not having a primary key in a table.

A primary key is a field that uniquely identifies each row in a table. It is important to have a primary key in a database table because it helps ensure the integrity of the data and allows for the easy identification and organization of records.

Not having a primary key can lead to several problems in a database. For example:

• It can make it difficult to identify and update specific records, as there is no way to uniquely identify them.
• It can lead to data inconsistencies, as multiple records may have the same data but there is no way to differentiate between them.
• It can make it difficult to enforce data integrity, as there is no way to ensure that each record has a unique value for a particular field.

Overall, not having a primary key can make it difficult to manage and maintain a database. It can lead to a variety of problems that can impact the accuracy and reliability of the data.

Advanced Data Model / Database Schema Design Questions

Advanced-level database design interview questions typically have topics that revolve around data modeling and various details; this is to test the depth of the candidate’s knowledge. Some advanced questions are below:

1. What is the difference between the star schema and the snowflake schema in database design?

Answer: In a star schema, a central fact table is surrounded by a number of smaller dimension tables. The fact table contains the measure or fact data, while the dimension tables contain descriptive attributes that provide context for the measures. A star schema is a simple and efficient design that is easy to understand and query.

A snowflake schema is a variation of the star schema. In this schema, the dimension tables are further normalized into multiple tables. This results in a more complex design with more tables, but it can provide more granularity and flexibility in querying the data.

The main difference between a star schema and a snowflake schema is the level of normalization. A star schema is more denormalized, with fewer tables and a simpler design. A snowflake schema is more normalized, with more tables and a more complex design. The choice between a star schema and a snowflake schema depends on the specific requirements and trade-offs of the database design.

2. What are some common database design errors?

Answer: There are several common database design errors:

• Not normalizing the data: Normalization is the process of organizing data in a way that reduces redundancy and dependency. Failing to normalize the data can result in data inconsistencies and update anomalies.
• Not choosing the right data types: Choosing the wrong data types for the data can result in data corruption or loss of precision. For example, using a small integer data type to store large numbers could cause data to be truncated or lost.
• Using too many indexes: Indexes can improve the performance of queries, but using too many indexes can negatively impact the performance of insert, update, and delete operations. It's important to carefully consider which indexes are necessary and which can be removed.
• Not defining primary keys: Every table should have a primary key, which is a unique identifier for each row. Failing to define a primary key can result in data inconsistencies and difficulties in querying and updating the data.
• Not defining foreign keys: Foreign keys are used to enforce relationships between tables. Failing to define foreign keys can result in data integrity issues and difficulties in querying the data.
• Not considering security: It's important to consider the security of the data and implement appropriate measures, such as encryption, access controls, and backup and recovery processes.

3. What is collation and how does it impact the data model design?

Answer: Collation refers to the rules and characteristics that define how data is sorted and compared in a database. Collation can impact data model design in several ways:

• It determines the order in which data is sorted and displayed, which can affect how data is queried and analyzed.
• It determines how data is compared, which can impact the way data is searched and indexed.
• It determines the case sensitivity of data, which can affect the way data is matched and joined.
• It determines the character set and language of data, which can impact how data is displayed and stored.

It's important to choose the appropriate collation for the data in the database, as it can impact the performance, functionality, and accuracy of the database.

4. What is a data mart?

Answer: A data mart is a subset of a data warehouse that is tailored to a specific business function or subject area. It provides targeted data to a specific group of users (i.e. a department or business unit) for decision-making purposes.

Data marts can be created from a subset of the data in a data warehouse or from scratch by extracting data from operational databases or other sources. They are typically smaller and simpler than data warehouses and are designed for use by a specific group.

5. What are the steps of designing a conceptual data model?

Answer: The key to this answer is focusing more on communication with the business stakeholders. However you decide to phrase your answer and approach the technical solution, it’s always important to keep close communication with the business team.

As for the technical steps, they generally include:

1. Define the purpose and scope of the data model. This involves identifying the business requirements and goals of the data model and determining the types of data that need to be included.
2. Identify the entities and their attributes. This involves identifying the key entities and the characteristics or attributes of each entity that need to be captured in the data model.
3. Define the relationships between the entities. This involves identifying the relationships between the entities and determining how the entities are connected to each other.
4. Refine and validate the data model. This involves reviewing and testing the data model to ensure that it accurately represents the data requirements and meets the needs of the business.
5. Document the data model. This involves creating a clear and concise document that describes the entities, attributes, and relationships in the data model. This document can be used as a reference for database design and implementation.

6. What’s the difference between clustering and partitioning?

Answer: Clustering and partitioning are both techniques that can be used to organize data in a database. However, they are used for different purposes and work in different ways.

Clustering is a method of organizing data in a database so that data that is frequently accessed together is physically stored together. This can improve the performance of queries that access the data, because the data is stored in a contiguous block rather than being scattered across the disk. Clustering is often used to improve the performance of data warehouses, where large amounts of data are accessed in bulk.

Partitioning is a method of dividing a large table or index into smaller, more manageable pieces, called partitions. Each partition is stored in a separate file or set of files, and can be managed and accessed independently of the other partitions. Partitioning is often used to improve the scalability and manageability of large databases by allowing different partitions to be processed in parallel and by making it easier to perform maintenance tasks on the data.

7. What is a junk dimension in data modeling?

Answer: A junk dimension is a type of dimension table used to store miscellaneous, low-cardinality attributes in a data warehouse. It is typically implemented as a single table with many columns and is used to store data that does not fit into any other dimension table. Junk dimensions are often used to store data about customer feedback or other low-cardinality data that is not used frequently but that you still want to keep.

Best of Luck on Your Database Design Interview!

There’s no way to know all potential database interview topics in depth. Even so, focus on your strengths and be open about your experience. Remember that a well-crafted resume can also help you highlight your skills and achievements during the interview process. So, be confident in your knowledge, but also brave enough to admit your less-knowledgeable areas. In my experience, this is much better than trying to always be correct.

Interviews are always varied. Fortunately, database schema design interview questions mostly follow this pattern of basic questions for beginner roles. Whether you’re called on to answer more advanced topics will always depend on your interviewer and their technical expertise.

There’s no way to know all potential database interview topics in depth. Even so, focus on your strengths and be open about your experience. Be confident in your knowledge, but also brave enough to admit your less-knowledgeable areas. In my experience, this is much better than trying to always be correct.

We hope the list of questions we shared above and the answers were helpful in getting you prepared for your next database job interview!