Data Modeling Interview Questions

Data Modeling Interview Questions and Answers

Data modeling is a process of managing and analyze data collected from different resources. The collected data may be redundant, so by using data modeling, we can avoid duplication of data and avoid waste of memory. Normally data modeling is a combination of designers, developers, and end users. When we talk about the profession, then, we people work with data such as DB engineers, BA, and developers.

Top 15 Data Modeling Interview Questions and Answers

Below are the top questions and answers to Data Modeling. These questions are helpful while giving mock tests or interviews.

Q1. Do you know the types of data modeling?

Answer:

There are three types of data modeling as follows:

Physical data model: This is used to store the data in the database in the form of schema, or we can say that framework is used to store the physical data in DB.
Conceptual data model: This is the second type of model, and it is used for the high-level users’ view.
Logical data model: Logical data model is a middle layer between the physical and conceptual data model, or we can say how we can represent the data.

Q2. Explain Table.

Answer:

We know that a table is a combination of rows and columns. Record or tuple is another name convention that is used for the table. In other words, we can say that it is a vertical and horizontal structure.

Q3. What is an ER diagram, and why do we use it?

Answer:

ER is nothing but the Entity-Relationship diagram, which represents the logical relationship between the entities with the help of different symbols such as boxes, arrows, and ellipses.

Q4. Do you know Surrogate Keys?

Answer:

The surrogate key is nothing but the primary key. Data modelers create the surrogate key, a useful tool for identifying records, creating SQL queries, and improving performance. We can use a surrogate key instead of the primary key.

Q5. Explain common errors which present in the data modeling.

Answer:

Creating data models that are too broad: The data model becomes more complicated as the number of tables exceeds 200, and failure chances may increase.

Unnecessary surrogate keys: It is used when our primary key cannot do the natural key task.

There may be times when the user has no idea what the company’s mission or goal is. If the data modeler lacks a solid understanding of the company’s business model, creating a specific business model is difficult, if not impossible.

Inappropriate denormalization: This strategy should not be used by users unless there is a compelling reason to do so. Although denormalization boosts read performance, it also generates redundant data that is difficult to maintain.

Q6. What are the two types of design schemas?

Answer:

There are two types of schemas: Star and Snowflake. A fact table is at the center of the Star schema, and multiple dimension tables surround it. A Snowflake schema has a snowflake-like appearance due to a higher normalization level.

Q7. Can you explain the slowly changing dimensions?

Answer:

Sometimes we need to manage the current as well as historical data, so at that time, we can use dimensions. Data modeling provides different dimensions, such as SCD 0 to SCD 3.

Q8. Do you know about Data Mart?

Answer:

The simplest form of data warehousing, a data mart, concentrates on a single business function. A subset of data warehouses known as data marts is focused on a specific line of the business or functional area of an organization (such as marketing, finance, or sales, for example). A variety of transactional systems, additional data warehouses, and even external sources bring data into data marts.

Q9. Explain granularity.

Answer:

The amount of data stored inside the table is called granularity in terms of high and low limits. Normally transaction level is one of the data types, and it can be found in high-limit granularity. On the other hand, we have low-limit data, which is present on the fact table called low-limit granularity.

Q10. Can you explain the difference between reverse and forward engineering?

Answer:

In forward engineering, data models can create DDL scripts. Databases can be made with DDL scripts. Data models are created using reverse engineering from scripts or a database. The user can engineer a database into a data model using some data modeling tools’ options that connect to the database.

Q11. What are the advantages of data modeling?

Answer:

By using data modeling, we can easily manage commercial data and implement normalization and structure.
By using data modeling, we can reduce the redundancy of data collected from different resources.
As per our requirement, we can design the database.
Fetching data from the warehouse is easy.

Q12. Can you explain the characteristics of the physical model?

Answer:

The data requirements for a single project or application are outlined in the physical data model. Depending on the project’s scope, it may be integrated with other physical data models.
Relationships between tables in the data model address the relationships’ cardinality and null ability.
Designed to work with a particular DBMS version, location, data storage, or technology that will be used in the project.
The assigned lengths, default values, and data types for columns should all be precise.
Access profiles, authorizations, views, indexes, and primary and foreign keys, among other things, are specified.

Q13. Do you know relation data modeling?

Ans: By using relational modeling, we can represent the database that we can access through normalization.

Q14. What is cardinality?

Answer:

It is nothing but the relationship between the two different entities.

Q15. Do you know about self-recursive relationships?

Answer:

Self-recursive means a standalone column is connected to the existing primary key.

Conclusion

From this article, we can understand the Data modeling Interview Question. It provides the basic idea about the mid-level and higher-level concepts of Data modeling. Data modeling Interview Question is a key point for every interview, or we can say that every technology.