Data Modeling Interview Questions With Example Answers

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Data modeling interview questions help employers select the right person for roles that include extensive data modeling, as a data scientist, for example. If you’re looking to capitalize on business trends that prioritize big data there’s never been a better time to examine data modeling. In this article, you can explore data modeling interview questions and example answers.

What are data modeling interview questions?

Data modeling interview questions are those designed for candidates to display introductory to expert level knowledge of data modeling principles and practices. In addition to emphasizing your skills in data modeling, these interview questions also seek to extract your experience with data modeling tools, principles and resources. Employers may ask you to describe an experience with data modeling or even share from a portfolio of data models you’ve created.

Why do employers ask data modeling interview questions?

 Employers use data modeling interview questions to determine if you have the technical data modeling skills for the role. Using the right set of data modeling interview questions employers can determine your level of experience with data models, where your data modeling skills lie and in what ways you can be developed. This helps employers choose the best candidate for the role they are trying to fill.

Why is data modeling important?

Data plays an important role in the businesses of today. Employees who know how to work with data are in high demand because Big Data is used to offer businesses insight that helps propel them into success. For this reason, skilled data professionals are important to the overall achievement of a business.

Data modeling comes with a number of benefits. It helps companies define data requirements, encourages professionals to understand the relationship between business systems, makes for more accuracy when measuring goals and more. Understanding the many ways data modeling helps a company grow and achieve, employers may ask data modeling interview questions. 

Common data modeling interview questions

Here are some common data modeling interview questions to help you prepare when interviewing for a job in big data:

What is data modeling?

This is a basic question that might introduce a line of questioning about data modeling. To answer the question, be sure to include a clear and concise definition that is simplified to include the most important points without a lot of jargon.

Example: ‘Data modeling occurs when a data professional makes a visual representation of the relationships between data and other entities. There are a number of representational schemas that data scientists use in data modeling, which follows the natural progression of a conceptual model, followed by a logical one and then a physical model.’

What is a logical data model?

Some businesses may apply a logical data model that governs overall business practices and principles. For this reason, candidates may have to explain what a logical data model is, and be able to show an understanding of it by providing a clear answer that offers a definition.

Example: ‘A logical model is sometimes considered the midway point in the data modeling process, because it shows more detail than a conceptual model but isn’t fully ready to be applied like a physical model. A logical data model contains names and relationships of entities and attributes, as well as primary and foreign keys. Business requirements are depicted in a logical model.’

How is a logical data model used?

People with exemplary knowledge of logical data models can thoroughly explain how they are used by pulling from their own experiences with the concept. Think about how you have used a logical data model when you answer this question.

Example: ‘It’s actually an implementation of a conceptual model in practice. It is used as a stepping stone to a physical model that can be implemented.’

Describe some different data models you have used?

There are a number of different data models that data scientists, and other data professionals, use to complete the job requirements of their position. In this answer, explain some of the data models you have used and why you chose to work with those models.

Example: ‘There are three types of data modeling: conceptual, logical and physical. In my experience as a data scientist, I’ve used all three models to implement the business requirements of a data structure. Data modeling can be displayed in a number of ways. I’ve commonly used hierarchical, relational and object-oriented views to show the relationship between data and other entities.’

Explain a recent data modeling project you worked on

In some cases, interviewers may ask you to draw directly from your experience and talk about specific projects. To be better prepared to answer a question like this one, you should come prepared with projects to discuss.

Example: ‘In a recent data modeling project, I helped to structure a database for a client by creating data models that showed a hierarchical relationship between parent and child accounts in her CMS system. This helped the account executives avoid pursuing the same accounts, and helped the information in the database be more accurate.’

Compare a star schema to a snowflake schema

These are two common schemas in data modeling. When you are asked to compare two or more schemas, you are given the opportunity to show you are knowledgeable about all of them. To do that, consider each schema in the context have how they relate to one another.

Example: ‘Star and snowflake schemas are similar. They both involve a centralized piece of data with dimensions and some normalization. In a star schema, all of the dimensions are normalized. However, in a snowflake schema, only some of the dimension tables are normalized.’

Of the two schemas mentioned, which do you like better and why?

This question seeks to get your professional opinion on a choice you might be faced with on the job. To answer this question, take a position on which schema has been more useful for you as a data professional by considering your industry and experience.

Example: ‘I prefer the simplicity of a star schema. While a snowflake schema may make slightly more efficient use of database space, I don’t think it’s a compelling enough argument to create a data model with more joins. Instead, a simplified data model is more practical.’