5 Data Modeling Interview Questions and Answers That Come Up Often in 2022
The way designers, programmers, and end users work together is made easier by data models. Data modeling is a way for many different kinds of organizations to manage and analyze their data. Data models are often used by professionals like database engineers, business analysts, and programmers. 5 Data Modeling Interview Questions
If you are interviewing for a job that involves data modeling, think about what you might be asked about it so you can give good answers that show off your skills and experience. This article looks at some common interview questions about data modeling and gives you some sample answers to help you feel ready for your interview.
Common questions about data modeling asked in job interviews and how to answer them
Try to answer the following questions using the STAR interview answer method as you think about them. This strategy helps you think of answers that show what you know and why you’re qualified by referring to specific experiences.
Using the STAR method, talk about a relevant situation, say what you needed to do, explain what you did, and say what happened as a result.
Here are five common questions about data modeling that you could be asked during an interview:
- Why would you want to use ERwin data modeler?
- What is data mart?
- What is a fake key, and when do you need one?
- Shouldn’t all databases be in third normal form?
1.What is the purpose of ERwin data modeler?
This ERwin tool interview question checks how up-to-date you are on the latest software and how well you know what it can do. It checks to see if a modeler can normalize a data model and help a client get the most accurate information.
Talk about what you think is its best function or feature, what makes it unique, and how you have used it.
When I was in charge of our last project, ERwin, a program used for data modeling, was shown to me. Our client needed a way to lower the cost of managing data. We used it to turn the physical mode into a real database, which made the whole process go faster. Changes to colors, fonts, layouts, and more can be made in ERwin, which is a plus. But the fact that it could be used to reverse engineer was very helpful to me.”
2.What does “data mart” mean?
This interview question about data modeling checks to see if you have a good grasp of the basics. Explaining the details of this question shows that you pay attention to the small things, and giving a focused answer shows that you know enough to know that end-user response time needs to be improved. You might consider things like:
Example: “In one of my first projects, a client asked us to find a cheaper and faster way to get to data. I used a structure called a “data mart,” which is only found in data warehouses and is a part of them. It is an easy-to-make summary of information about a certain part of a business or organization. It saved our client money because it costs less to run than a full data warehouse and made it easy for them to get to the data they used most often. I often use it to keep track of goods or sales, but only in a certain part of the building.
3.What is a fake key, and when should you use it?
This question about the details of the job shows if you can help clients organize their data so they can find what they need quickly. Your answer shows how well you understand how data modeling is different from storage modeling.
It shows that you are sure you can make models that are stable and can store information that clients can access.
“As chief engineer at company ABC, one of my jobs was to teach new engineers how to use the artificial key. They had to find a way to store information that would stay the same even if the values in other fields changed. A natural key is one that happens on its own as part of the database and changes at the same time as the others. A derived key, which is also called an artificial key or a surrogate key, breaks the principle of stability.
It doesn’t do much more for the system than what it has already done. We used the surrogate key to store useless information for our client. It had spaces for the Social Security numbers, home addresses, phone numbers, and email addresses of each employee. When our client merged with a partner, we used the surrogate key to make it easy to move the information. My entry-level engineers picked it up quickly, and we later used it for our client to keep track of employee performance increases as well.
4.Should all databases use the third normal form?
With questions like this, you are being tested on how well you can normalize databases to cut down on duplicate data. It checks if you know the difference between normalized and denormalized data and how to use data modeling to make sure data is correct and not duplicated.
Even when I was a new engineer, 3NF was one of the things I was good at. Most organization databases are in third normal function (3NF) to get rid of duplicate information and make it easier to find what you need. One of our retail clients wanted a simple way to keep track of information about their customers. Even if the same customer bought something more than once, each purchase was counted as a new person in the original data model.
Using 3NF, we were able to make a new column called “Alias” where each customer was only counted once, but their purchases were still kept track of. Using denormalizing and 3NF to normalize, we helped our client better keep track of spending patterns and inventory. Even though 3NF is used by many databases, it is not a must.
5.Can you show how the star schema and the snowflake schema are alike and different?
How you answer this question will show how well you know how to put information in order. A database’s “data schema” is the formal language used by the management system to describe the structure of the database.
A “schema” is a plan for how the data will be put together. Around the fact table in Star schema are several dimension tables.
Most of the time, the star schema and the snowflake schema store the same information. Normalization, on the other hand, has put the information in order. In your answer, tell us when each schema should be used.
“The most common multidimensional models in a data warehouse are the star schema and the snowflake schema. They can both store similar kinds of data, but there is one big difference: star schema is denormalized and snowflake schema is normalized. A client of mine looked at data with star schema, but it was wrong because of redundancy. At the heart of star schema is a fact table or more than one fact table.
These fact tables link to a set of dimension tables. Star schema separates fact data, which are usually numbers, like price, percentage, and weight, from dimensional data, which are things like color, name, and location. They work well for quick searches and can help a lot of people get to basic information. Even though the denormalized structure tries to stop anomalies from happening, it doesn’t do a good job of making sure that the data is correct. This made it hard for my client to figure out what the data meant.
I decided to give their model a snowflake-like look. Snowflake schemas are multidimensional and look like snowflakes, which is how they got their name. They store the same information that star schema does, but they get rid of duplicate information by using normalization. In this schema, the shape of a snowflake is made by splitting dimensional tables into more than one. My client could easily keep track of what customers bought and use that information to make business and marketing decisions. This fixed the problem and made their analysis of the data a lot easier. The data in star schema are put back to normal by snowflake schema. This method made the structure more complicated for our client because it made it more uniform, but the results of the data analysis were very good.
