You have solid credentials behind you; your qualifications speak for themselves, but an interview can throw all those things out the window. There is more to landing a job than what’s on your resume; employers want to see confidence, alertness, know-how, and someone who’ll be a valuable member of the team. It’s a daunting prospect because all interviews are nerve-wracking even when you’re more than qualified for the position.
You must be at the top of your game to land a great data modeling position and there are always questions during the interview that catch you off-guard. So, what questions should you expect to answer in your next interview?
What Are the Basic Data Model Types?
This is a standard question the interviewer is likely to ask when you want to land a data modeling job. It’s essential you answer this question with a clear and precise answer. For instance, the three main data model types are physical data, conceptual, and logical data models. However, it is essential to go into as much detail as you can.
For example, you can talk about the logical data model. You could say how the data is torn between theoretical and physical data models. This allows you to find logical data representations. For physical data models, you could talk about how this is the framework where data is stored within a database. The conceptual data model, say it focuses more on the data based on the user’s view. Going into specifics can be useful.
What Is the Data Table?
Another very basic question, but one that you’re likely to be asked in an interview. You know the data table has columns and rows that the data is stored on. The fields (columns,) arrange the data vertically, whereas rows show the data horizontally. You should try to be as specific as you can so that you can highlight your expansive knowledge.
How Is Normalization Used by a Data Modeler?
You should explain how data modelers use normalization. For instance, the purposes of normalization include, reducing the complexity of data and the relationship of the data. It can also get rid of unnecessary data, keeping the most relevant pieces. Another purpose of normalization is to ensure the data is logically stored.
What is the Purpose of Denormalization?
You not only have to explain the purpose of denormalization but say what it is exactly. For instance, talk about how denormalization is in fact a technique and how useless data moves onto a normalized database. Through denormalization, it ensures read functions are improved, albeit, through the sacrifice of performance.
What Is a Surrogate Key Used For?
Surrogate keys are also classed as the primary key. It deals with number-related factors. It replaces a natural key. So, talk about how data modelers create surrogate keys to identify records, enhance performance, and build SQL inquiries. You should be as expansive as you can to ensure you showcase your skills to the fullest.
In Data Models, What Type of Relationships are Present?
You want to talk about the data model in greater detail and highlight the critical relationship types. For instance, you have self-recursive, identifying, and non-identifying relationship types; talk about these in detail. Recursive relationships are found within a table and is one of the columns. It connects to the surrogate key also. Whereas an identifying relationship is a line that connects child and parent tables. This creates the identifying relationship line.
The non-identifying relationship focuses on data that doesn’t form the surrogate key. It signals there isn’t an identifying relationship. It’s important to talk about these things as it can show your range of knowledge to the interviewer.
In Data Modeling, What Common Errors Are You Likely to Find?
You can run into many different errors in data modeling; you need to highlight a few of the more common errors. For instance, talk about unnecessary primary keys and how their function comes into play when a natural key cannot take on the role of the surrogate key. You could also talk about the missing purpose. In data modeling, the purpose could be missing, and this occurs when the goal of the business isn’t known to the user. The data modeler won’t be able to create a business model because of the lack of a clear understanding of the business.
Can You Explain the Different Design Schemas?
You have the Snowflake and Star design schemas. It’s crucial to highlight the main function of each of these. For example, you could talk about how the Snowflake schema has a higher level of normalization than the Star schema. This is how this design schema is known as the Snowflake because of its unique shape. The Star schema focuses on a fact table that has several tables around it.
Do You Know What Granularity Is?
You will find most interviewers ask this question; however, it’s an important one to get right. It’s important to highlight the role of granularity. This represents the information within a data table. It can be classed as low or high-granularity data. Low-level granularity focuses on data in fact tables. High-granularity data focuses on transactional data.
What is a Data Mart?
This is a standard question that’ll crop up in a data modeling interview. So, you need to explain what it is. You could talk about how the data mart focuses on one specific area within a business. It is a data warehouse and deals with things such as sales, marketing, and finance. It can be an important aspect of any business model.
What Are Super and Sub-Type Entities?
Data models have entities, and each can be grouped into super or sub-type entities. It depends on the specific data requirements and the features used also. There can be several sub-type entities, or many grouped together to create a super-type of an entity.
Be Confident Going into the Interview
Data modeling interview questions can be varied; some interviewers will keep things brief while others will have an extensive list of questions to ask. It depends on the individual interviewer and the position you apply for. You should, however, try to answer all questions as honestly as you can, and as specific as you can also. It is important to showcase your knowledge, of course, keep answers short but precise. Now you know a few potential questions you’ll face in a data modeling interview, hopefully, you’ll feel more confident.