If you are looking for the most essential data modeler interview questions and answers for freshers and experienced, you have reached the right place today. When plenty of technology companies use it worldwide, Data Modeling enjoys a share of 15.43 % in the global market. Indeed, you have the opportunity to move ahead in your career with Data Modeling skills and a set of top Data Model interview questions and answers. If Data Modeling is new to you or you want to know the Data Modeling concept in depth, then join the SQL Server Training & Certification course at JanBask Training to give a new definition to your career.
JanBask Training mentors have prepared a list of frequently asked data model interview questions that will help you in getting your dream job as a Data Modeling Architect.
Ans:- A Data Model is the conceptual representation of a database's business requirements or physical attributes that help communicate the business requirements with clients.
Ans:- The approach that is used to prepare a data model is called Data Modeling.
Ans:- It could be the physical data model and the logical data model, enterprise data model, conceptual data model, relational data model, OLTP data model, etc.
Ans:- A physical data model contains Table, key constraints, columns, unique key, foreign key, default values, indexes, etc.
Ans:- A logical data model contains an entity, attributes, primary key, alternate key, Inversion keys, rule, definition, business relation, etc.
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Ans:- A physical data model contains the physical attributes of a database. The database performance, physical storage, and indexing strategy are essential considerations of a physical data model. The main component here is a database. The approach that is used for creating a physical data model is called physical data modeling.
Ans:- A logical data model is related to the business requirements and is used to implement the data. The approach that is used for creating a logical data model is called logical data modeling.
Logical Data Model
Physical Data Model
1). A logical data model will tell the business requirements logically. 2). A logical data model is responsible for the actual implementation of data stored within a database. 3). A logical data model contains an entity, attributes, primary key, alternate key, Inversion keys, rule, definition, business relation, etc.
1). A physical data model will tell you about the target database source and its properties. 2). A physical data model will tell you how to create a new database model from the existing one and apply the referential integrity. 3). A physical data model contains Table, key constraints, columns, unique key, Foreign Key, default values, indexes, etc.
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Ans:- A database consists of multiple rows and columns, called a table. Further, each column has a specific datatype, and constraints are set for the columns based on conditions.
Ans:- A Column is defined as the vertical alignment of data and information stored for that particular column.
Ans:- A row is a set of tuples or records, or it could be taken as the horizontal arrangement of the data.
Ans:- ER is a logical representation of entities and defines the relationship among entities. Entities are given inboxes, and the relationships are given by arrows.
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Ans:- The primary key constraint is set on a column to avoid null values or duplicate values. In simple words, a column containing unique items can be defined as the primary key constraint. It could be the bank number, security number, or more.
Ans:- When more than a single column can be defined as the primary key constraint, it becomes composite.
Ans:- The Primary key can be defined for the parent table, and the foreign key is set for the child table. The foreign key constraint always refers to the primary key constraint in the main table.
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Ans:- When a numerical attribute is enforced on a primary key in a table, it is called the surrogate key. This could be defined as a substitute for natural keys. Instead of generating primary or foreign keys, surrogate keys are generated by the database, and they are further helpful in designing the SQL queries.
Q17). Why is a composite word added before any key constraint?
Ans:- When the same constraint is enforced on multiple columns, the composite word is added before that particular key constraint.
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Ans:- These are identifying, non-identifying, and self-recursive relationships in a data model.
Ans:- As you know, the parent and child table is connected with a thin line. When the referenced column in a child table is a part of the primary key in the parent table, then those relationships are drawn by a thick line and named as the identifying relationships in a data model.
Ans:- In most cases, a parent table and the child table are both connected with a thin line. When the referenced column in a child table is not a part of the primary key in the parent table, then those relationships are drawn by a dotted line, and it is named the non-identifying relationships in a data model.
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Ans:- Cardinalities are used to define relationships; they could be one-to-one, one-to-many, many-to-many, etc. The higher the cardinality value, the more unique values within a column.
Ans:- This is a standalone column in the table connected to the same table's primary key and is named the self-recursive relationship here.
Ans:- Here, all-important entities are defined as related to an enterprise. You should first understand the essential data elements and their possible relationship. This relationship is defined as enterprise data modeling. To understand this model in the best areas, you should divide the data models into subject areas.
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Ans:- The relational data model is the visual representation of data objects within a database. The approach used to create a relational data model is called relational data modeling.
Ans:- OLTP or Online Transaction Processing is an approach where the data model is specially created for the transactions, and the approach is named the OLTP data modeling.
Ans:- It will give you detailed information related to the entity, attributes, or relationships between them.
Ans:- This is a rule imposed on the data. Different types of constraints could be unique, null values, foreign keys, composite keys or check constraints, etc.
Ans:- This constraint is added to avoid duplicate values within a column.
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Ans:- A check constraint is helpful in defining the range of values within a column.
Ans:- An Index is composed of a set of columns or a single column that is needed for fast retrieval of data.
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Ans:- A sequence could be defined as the database object needed to create a unique number.
Ans:- To simplify the data based on standard rules, database normalization is needed.
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Ans:- The purpose of normalization is to:
Ans:- Denormalization is the technique of adding redundant data to an already normalized database. This step improves read performance at the expense of write performance.
Ans:- Metadata describes data about the data. Shows what data is actually stored in the database system.
Ans:- The data mart is a summary version of the data warehouse designed to be used by specific departments, units, or user groups within your organization. B. Marketing, sales, HR, or finance.
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Ans:- OLTP System Example:
Ans:- Types of normalization are
Ans:- Forward engineering is a technical term used to automatically transform a logical model into a physical working device.
Ans:- A data cube that stores data as aggregates. It helps users to analyze data quickly. PDAP data is stored in a way that facilitates reporting.
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Ans:- A snowflake schema is an arrangement of dimension and fact tables. Both tables are typically further divided into separate dimension tables.
Ans:- A sequence clustering algorithm collects a set of data containing paths and events that are similar or related to each other.
Ans:- Discrete data is finite or defined data. B. Gender, phone number. Continuous data is data that changes continuously and regularly, for example, Age.
Ans:- Time Series Algorithms are methods of predicting continuous values of data in a table. For example, employee performance can predict benefits and impact.
Ans:- BI (Business Intelligence) is the set of processes, architecture, and technologies that transform raw data into meaningful information that drives meaningful business action. A suite of software and services for transforming data into actionable information and knowledge.
Ans:- Data warehousing is the process of collecting and managing data from a variety of sources. It provides meaningful insights into the enterprise. Data warehousing is typically used to connect and analyze data from disparate sources. It is the core of a BI system designed for data analysis and reporting.
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Ans:- A junk dimension combines two or more related cardinalities into a single dimension. These are typically boolean or flag values.
Ans:- The frequency of data collection is the frequency of data collection. It also goes through different stages.
These phases are-
Ans:- Cardinality is a numeric attribute of the relationship between two entities or an entity set.
Ans:- The essential fundamental relationships of the different types are
Ans:- Granularity represents the level of information stored in a database table. To make it high or low, you can use a table that contains a transaction-level table and a fact table. Particle size is also a measure of the smallest dataset that each component/task/application can handle individually.
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Ans:- Data sparseness refers to the number of empty cells in the database. It represents the amount of data available in a particular dimension of the data model. Insufficient information consumes a lot of space to store the aggregate.
Ans:- Hashes are helpful for searching index values to get the data you need. It is used to calculate the direct location of the data using the index.
Ans:- OLAP stands for On-Line Analytical Processing and is designed to help managers, executives, and analysts gain insights faster, more confidently, more consistently, and more interactively. It is a type of technology that makes OLAP used in intelligent solutions, including planning, budgeting, analysis, forecasting, simulation models, and more. OLAP helps clients consider multiple dimensions and perform analysis to provide insights that help them make better decisions.
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Ans:- A recursive relationship occurs when there is a relationship between the entity and itself. These relationships are complex and require a more sophisticated approach to mapping data to schemas. Consider the case where a doctor is flagged as a healthcare provider in a healthcare database. Now, when a doctor gets sick, he has to see another doctor as a patient, leading to a recursive relationship. To do this, add a foreign key to the health center number on each patient record. It should be noted that such entity-related recursion has an exit path.
We hope these Data Modeler interview questions have given you a glimpse of what kind of questions can be asked in a Data Modeling interview and what a Data Modeler does. So, if you’re planning to accelerate your data modeler career, sign up JanBask Training’s Online SQL Training Program, which will boast a half dozen courses, including in-demand skills and tools and real-life projects.
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