Of course, if you want to pass your next job interview, you must first make sure that your qualifications are worthy. But you can do more to assess the chances in your favor. Knowing your stuff is essential, yes, but it is also being prepared.

In this context, we are talking about readiness for the questions you are likely to encounter in the interview. All the knowledge in the world will be useless if you do not know where to apply it. If you know what questions you will be asked, you can review the material and be ready with the best answers.

So today we will pay attention to the most frequently asked questions for a data modeling interview. We will start with the main issues, then move on to intermediate ones, 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?

The data model organizes different elements of data and standardizes how they relate to each other and the properties of the object in the real world. So logically then data modeling is the process of creating these data models.

Data models are made up of objects, and objects 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 actors.

Each object has attributes – details that users want to track. For example, the client name is an attribute.

Other than that, let’s check out these data modeling interview questions!

Basic interview questions for data modeling

1. What are the three types of data models?

The three types of data models:

  • Physical data model – This is where the frame or schema describes how the data is physically stored in the database.
  • Conceptual data model – This model focuses on the user’s high-level view of the data in question
  • Logical data models – These are located between physical and theoretical data models, allowing logical data representation to exist separately from physical storage.

2. What is a table?

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

3. What is normalization?

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

4. What does the data developer use normalization for?

The goals of normalization are:

  • Remove useless or redundant data
  • Reduce data complexity
  • Provide links between tables in addition to the data in the tables
  • Make sure that the data is dependent and that the data is stored logically.

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5. So, what is denormalization and what is its purpose?

Denormalization is a technique in which redundant data is added to an already normalized database. The procedure improves reading performance by sacrificing write performance.

6. What does ERD mean and what is it?

ERD stands for Object Relationship Diagram and is a logical representation of an object that defines the relationships between objects. The subjects are in boxes, and the arrows symbolize the relationship.

7. What is the definition of a surrogate key?

A surrogate key, also known as a primary key, imposes numeric attributes. This surrogate key replaces natural keys. Instead of having primary or composite primary keys, modeling data creates the surrogate key, which is a valuable tool for identifying records, building SQL queries, and increasing productivity.

8. What are the critical types of relationships found in a data model? Describe them.

The main types of relationships are:

  • Identifying. The link line usually connects parent and child tables. But if the reference column of a child table is part of the primary key of the table, the tables are linked by a thick line, which means an identifying link.
  • Non-identifying. If the child column reference column is NOT part of the primary key of the table, the tables are linked by a dotted line, which means a link without identification.
  • Self-recursive. A recursive link is a stand-alone column in a table associated with the primary key in the same table.

9. What is a corporate data model?

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

Interview questions for intermediate data modeling

10. What are the most common mistakes you may encounter when modeling data?

These are the errors that are most likely to occur during data modeling.

  • Building too broad data models: If tables run higher than 200, the data model becomes more complex, increasing the likelihood of failure
  • Unnecessary surrogate keys: Surrogate keys should only be used when the natural key cannot act as a primary key
  • The goal is missing: situations may arise in which the consumer has no idea about the mission or purpose of the business. It is difficult, if not impossible, to create a specific business model if the data modeler does not have a working understanding of the company’s business model.
  • Improper denormalization: Consumers should not use this tactic unless there is an excellent reason for it. Denormalization improves reading performance, but creates redundant data, which is a challenge to maintain.

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11. Explain the two different design schemes.

The two design schemes are called Star schema and Snowflake schema. The Star schema has a fact table centered with multiple dimension tables around it. The snowflake pattern is similar, except that the level of normalization is higher, which makes the pattern look like a snowflake.

12. What is a slowly changing dimension?

These are dimensions used to manage both historical data and current data in data storage. There are four different types of slow-changing sizes: SCD type 0 to SCD type 3.

13. What is a Data Mart?

The data showcase is the simplest set of data storage and is used to focus on one functional area of ​​each business. Data showcases are a subset of data warehouses focused on a specific business or functional area of ​​an organization (eg marketing, finance, sales). The data enters the data showcases through a set of transaction systems, other data warehouses or even external sources.

14. What is granularity?

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

15. What is data scarcity and how does it affect aggregation?

The sparseness of the data determines how much data we have for a particular dimension or object of the model. If there is not enough information stored in the dimensions, then more space is needed to store these aggregations, leading to a huge, cumbersome database.

16. What are subtypes and supertypes?

Objects can be divided into several sub-objects or grouped by specific characteristics. Each subproject has corresponding attributes and is called a subtype object. Attributes common to each object are placed in a higher or super level object, which is why they are called supertype objects.

17. What is the significance of metadata in the context of data modeling?

Metadata is defined as “data data”. In the context of data modeling, this is the data that covers what types of data are in the system, what they are used for and who uses them.

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Interview questions for advanced data modeling

18. Should all databases be displayed in 3NF?

No, this is not an absolute requirement. However, denormalized databases are easily accessible, easier to maintain, and less redundant.

19. What is the difference between forwarding and reverse engineering in the context of data models?

Pre-engineering is a process in which data definition language (DDL) scripts are generated by the data model itself. DDL scripts can be used to create databases. Reverse engineering creates database models from databases or scripts. Some data modeling tools have options that connect to the database, allowing the user to design a database in a data model.

20. What are recursive links and how do you correct them?

Recursive connections occur when there is a connection between an object and itself. For example, a doctor may be in the health center’s database as a care provider, but if the doctor is ill and enters as a patient, this leads to a recursive connection. You will need to add a foreign key to the health center number in each patient’s file.

21. What is a confirmed dimension?

If a dimension is confirmed, it is 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 scheme, which means they can evolve and change as quickly as needed
  • NoSQL databases have partitioning, partitioning and distribution of data to smaller databases for faster access
  • They offer rejection and better recovery options thanks to replication
  • Easily scale, grow or shrink if needed

23. What is an unwanted dimension?

This is a grouping of attributes with low cardinality such as indicators and flags, removed from other tables and subsequently “discarded” in a table with abstract dimensions. They are often used to initiate rapidly changing dimensions in data warehouses.

24. If a unique constraint is applied to a column, will it generate an error if you try to put two zeros in it?

No, it will not happen, because the zero values ​​of the error are never equal. You can put multiple zeros in a column and not generate an error.

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Do you want training in data modeling?

I hope these data modeling interview questions have given you an idea of ​​the type of questions that 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 model, then you’ll want to check out 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 receive hands-on exposure to key technologies, including R, SAS, Python, Tableau, Hadoop and Spark. Experience world-class industry-leading training in the most sought-after data science and machine learning skills.

The program boasts half a dozen courses, over 30 sought-after skills and tools and more than 15 real-life projects. So check out Simplilearn’s resources and start this new data modeling career!


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