It’s a given that if you want to ace your next job interview, you first need to make sure your qualifications are worthy. But there is more you can do to help weigh the odds in your favor. Knowing your stuff is essential, yes, but so is being prepared.

In this context, we are talking about being ready for the questions that you will most likely face in the interview. All the knowledge in the world will be useless if you don’t know where to apply it. If you know what kinds of questions you will be asked, you can review the material and be ready with the best answers.

So today, we will spotlight the most commonly asked data modeling interview questions. We will start with basic questions, then work our way up through intermediate, followed by advanced ones.

But before we look at the questions, let’s take a moment and ask, “what is a data model?”

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What is a Data Model?

A data model organizes different data elements and standardizes how they relate to one another and real-world entity properties. So logically then, data modeling is the process of creating those data models.

Data models are composed of entities, and entities are the objects and concepts whose data we want to track. They, in turn, become tables found in a database. Customers, products, manufacturers, and sellers are potential entities.

Each entity has attributes—details that the users want to track. For instance, a customer’s name is an attribute.

With that out of the way, let’s check out those data modeling interview questions!

Basic Data Modeling Interview Questions

1. What Are the Three Types of Data Models?

The three types of data models:

Physical data model - This is where the framework or schema describes how data is physically stored in the database.
Conceptual data model - This model focuses on the high-level, user’s view of the data in question
Logical data models - They straddle between physical and theoretical data models, allowing the logical representation of data to exist apart from the physical storage.

2. What is a Table?

A table consists of data stored in rows and columns. Columns, also known as fields, show data in vertical alignment. Rows also called a record or tuple, represent data’s horizontal alignment.

3. What is Normalization?

Database normalization is the process of designing the database in such a way that it reduces data redundancy without sacrificing integrity.

4. What Does a Data Modeler Use Normalization For?

The purposes of normalization are:

Remove useless or redundant data
Reduce data complexity
Ensure relationships between the tables in addition to the data residing in the tables
Ensure data dependencies and that the data is stored logically.

5. So, What is Denormalization, and What is its Purpose?

Denormalization is a technique where redundant data is added to an already normalized database. The procedure enhances read performance by sacrificing write performance.

6. What Does ERD Stand for, and What is it?

ERD stands for Entity Relationship Diagram and is a logical entity representation, defining the relationships between the entities. Entities reside in boxes, and arrows symbolize relationships.

7. What’s the Definition of a Surrogate Key?

A surrogate key, also known as a primary key, enforces numerical attributes. This surrogate key replaces natural keys. Instead of having primary or composite primary keys, data modelers create the surrogate key, which is a valuable tool for identifying records, building SQL queries, and enhancing performance.

8. What Are the Critical Relationship Types Found in a Data Model? Describe Them.

The main relationship types are:

Identifying. A relationship line normally connects parent and child tables. But if a child table’s reference column is part of the table’s primary key, the tables are connected by a thick line, signifying an identifying relationship.
Non-identifying. If a child table’s reference column is NOT a part of the table’s primary key, the tables are connected by a dotted line, signifying a no-identifying relationship.
Self-recursive. A recursive relationship is a standalone column in a table connected to the primary key in the same table.

9. What is an Enterprise Data Model?

This is a data model that consists of all the entries required by an enterprise.

Intermediate Data Modeling Interview Questions

10. What Are the Most Common Errors You Can Potentially Face in Data Modeling?

These are the errors most likely encountered during data modeling.

Building overly broad data models: If tables are run higher than 200, the data model becomes increasingly complex, increasing the likelihood of failure
Unnecessary surrogate keys: Surrogate keys must only be used when the natural key cannot fulfill the role of a primary key
The purpose is missing: Situations may arise where the user has no clue about the business’s mission or goal. It’s difficult, if not impossible, to create a specific business model if the data modeler doesn’t have a workable understanding of the company’s business model
Inappropriate denormalization: Users shouldn’t use this tactic unless there is an excellent reason to do so. Denormalization improves read performance, but it creates redundant data, which is a challenge to maintain.

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11. Explain the Two Different Design Schemas.

The two design schema is called Star schema and Snowflake schema. The Star schema has a fact table centered with multiple dimension tables surrounding it. A Snowflake schema is similar, except that the level of normalization is higher, which results in the schema looking like a snowflake.

12. What is a Slowly Changing Dimension?

These are dimensions used to manage both historical data and current data in data warehousing. There are four different types of slowly changing dimensions: SCD Type 0 through SCD Type 3.

13. What is Data Mart?

A data mart is the most straightforward set of data warehousing and is used to focus on one functional area of any given business. Data marts are a subset of data warehouses oriented to a specific line of business or functional area of an organization (e.g., marketing, finance, sales). Data enters data marts by an assortment of transactional systems, other data warehouses, or even external sources.

14. What is Granularity?

Granularity represents the level of information stored in a table. Granularity is defined as high or low. High granularity data contains transaction-level data. Low granularity has low-level information only, such as that found in fact tables.

15. What is Data Sparsity, and How Does it Impact Aggregation?

Data sparsity defines how much data we have for a model’s specified dimension or entity. If there is insufficient information stored in the dimensions, then more space is needed to store these aggregations, resulting in an oversized, cumbersome database.

16. What Are Subtype and Supertype Entities?

Entities can be broken down into several sub-entities or grouped by specific features. Each sub-entity has relevant attributes and is called a subtype entity. Attributes common to every entity are placed in a higher or super level entity, which is why they are called supertype entities.

17. In the Context of Data Modeling, What is the Importance of Metadata?

Metadata is defined as “data about data.” In the context of data modeling, it’s the data that covers what types of data are in the system, what it’s used for, and who uses it.

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Advanced-Data Modeling Interview Questions

18. Should All Databases Be Rendered in 3NF?

No, it’s not an absolute requirement. However, denormalized databases are easily accessible, easier to maintain, and less redundant.

19. What’s the Difference Between forwarding and Reverse Engineering, in the Context of Data Models?

Forward engineering is a process where Data Definition Language (DDL) scripts are generated from the data model itself. DDL scripts can be used to create databases. Reverse Engineering creates data models from a database or scripts. Some data modeling tools have options that connect with the database, allowing the user to engineer a database into a data model.

20. What Are Recursive Relationships, and How Do You Rectify Them?

Recursive relationships happen when a relationship exists between an entity and itself. For instance, a doctor could be in a health center’s database as a care provider, but if the doctor is sick and goes in as a patient, this results in a recursive relationship. You would need to add a foreign key to the health center’s number in each patient’s record.

21. What’s a Confirmed Dimension?

If a dimension is confirmed, it’s attached to at least two fact tables.

22. Why Are NoSQL Databases More Useful than Relational Databases?

NoSQL databases have the following advantages:

They can store structured, semi-structured, or unstructured data
They have a dynamic schema, which means they can evolve and change as quickly as needed
NoSQL databases have sharding, the process of splitting up and distributing data to smaller databases for faster access
They offer failover and better recovery options thanks to the replication
It’s easily scalable, growing or shrinking as necessary

23. What’s a Junk Dimension?

This is a grouping of low-cardinality attributes like indicators and flags, removed from other tables, and subsequently “junked” into an abstract dimension table. They are often used to initiate Rapidly Changing Dimensions within data warehouses.

24. If a Unique Constraint Gets Applied to a Column, Will It Generate an Error If You Attempt to Place Two Nulls in It?

No, it won’t, because null error values are never equal. You can put in numerous null values in a column and not generate an error.

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Do You Want Data Modeling Training?

I hope these Data modeling interview questions have given you an idea of the kind of questions can be asked in an interview. So, if you’re intrigued by what you’ve read about data modeling and want to know how to become a data modeler, then you will want to check the article that shows you how to become one.

But if you’re ready to accelerate your career in data science, then sign up for Simplilearn’s Data Scientist Course. You will gain hands-on exposure to key technologies, including R, SAS, Python, Tableau, Hadoop, and Spark. Experience world-class training by an industry leader on the most in-demand Data Science and Machine learning skills.

The program boasts a half dozen courses, over 30 in-demand skills and tools, and more than 15 real-life projects. So check out Simplilearn’s resources and get that new data modeling career off to a great start!

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