- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
Top 20 Most Popular Data Modelling Interview Questions & Answers [For Beginners & Experienced]
Updated on 25 November, 2022
7.02K+ views
• 10 min read
Data Science is one of the most lucrative career fields in the present job market. And as competition picks up, job interviews are also getting more innovative by the day. Employers want to test candidates’ conceptual knowledge and practical understanding of relevant subjects and technology tools. In this blog, we will discuss some relevant data modelling interview questions to help you make a powerful first impression!
Top Data Modelling Interview Questions and Answers
Here are 20 data modelling interview questions along with the sample answers that will take you through the beginner, intermediate, and advanced levels of the topic.
1. What is Data Modeling? List the types of data models.
Data modelling involves creating a representation (or model) of the data available and storing it in a database.
A data model comprises entities (such as customers, products, manufacturers, and sellers) that give rise to objects and attributes that users want to track. For instance, a Customer Name is an attribute of the Customer entity. These details further take the shape of a table in a database.
There are three basic types of data models, namely:
- Conceptual: Data architects and business stakeholders create this model to organise, scope, and define business concepts. It dictates what a system should contain.
- Logical: Put together by data architects and business analysts, this model maps the technical rules and data structures, thus determining the system’s implementation regardless of a database management system or DBMS.
- Physical: Database architects and developers create this model to describe how the system should operate with a specific DBMS.
2. What is a Table? Explain Fact and Fact Table.
A table holds data in rows (horizontal alignments) and columns (vertical alignments). Rows are also known as records or tuples, whereas columns may be referred to as fields.
A fact is quantitative data like “net sales” or “amount due”. A fact table stores numerical data as well as some attributes from dimensional tables.
Check out our data science online courses to upskill yourself
3. What do you mean by (i) dimension (ii) granularity (iv) data sparsity (v) hashing (v) database management system?
(i) Dimensions represent qualitative data such as class and product. Therefore, a dimensional table containing product data will have attributes like the product category, product name, etc.
(ii) Granularity refers to the level of information stored in a table. It can be high or low, with the tables containing transaction-level data and fact tables, respectively.
(iii) Data sparsity means the number of empty cells in a database. In other words, it states how much data we have for a particular entity or dimension in the data model. Insufficient information leads to large databases as more space is required to save the aggregations.
(iv) The hashing technique helps search index values for retrieving desired data. It is used to calculate the direct location of data records with the help of index structures.
(v) A Database Management System (DBMS) is software comprising a group of programs for manipulating the database. Its primary purpose is to store and retrieve user data.
4. Define Normalisation. What is its purpose?
The normalisation technique divides larger tables into smaller ones, linking them using different relationships. It organises tables in a way that minimises the dependency and redundancy of the data.
There can be five types of normalisations, namely:
- First normal form
- Second normal form
- Third normal form
- Boyce-Codd fourth normal form
- Fifth normal form
5. What is the utility of denormalisation in data modelling?
Denormalisation is used to construct a data warehouse, especially in situations having extensive involvement of tables. This strategy is used on a previously normalised database.
6. Elucidate the differences between primary key, composite primary key, foreign key, and surrogate key.
A primary key is a mainstay in every data table. It denotes a column or a group of columns and lets you identify a table’s rows. The primary key value cannot be null. When more than one column is applied as a part of the primary key, it is known as a composite primary key.
On the other hand, a foreign key is a group of attributes that allows you to link parent and child tables. The foreign key value in the child table is referenced as the primary key value in the parent table.
A surrogate key is used to identify each record in those situations where the users do not have a natural primary key. This artificial key is typically represented as an integer and does not lend any meaning to the data contained in the table.
7. Compare the OLTP system with the OLAP process.
OLTP is an online transactional system that relies on traditional databases to perform real-time business operations. The OLTP database has normalised tables, and the response time is usually within milliseconds.
Conversely, OLAP is an online process meant for data analysis and retrieval. It is designed for analysing large volumes of business measures by category and attributes. Unlike OLTP, OLAP uses a data warehouse, non-normalised tables and operates with a response time of seconds to minutes.
8. List the standard database schema designs.
A schema is a diagram or illustration of data relationships and structures. There are two schema designs in data modelling, namely star schema and snowflake schema.
- A star schema comprises a central fact table and several dimension tables that are connected to it. The primary key of the dimension tables is a foreign key in the fact table.
- A snowflake schema has the same fact table as the star schema but at a higher level of normalisation. The dimension tables are normalised or have multiple layers, which resembles a snowflake.
9. Explain discrete and continuous data.
Discrete data finite and defined, such as gender, telephone numbers, etc. On the other hand, continuous data changes in an ordered manner; for example, age, temperature, etc.
10. What are sequence clustering and time series algorithms?
A sequence clustering algorithm collects:
- Sequences of data having events, and
- Related or similar paths.
Time series algorithms predict continuous values in data tables. For instance, it can forecast the sales and profit figures based on employee performance over time.
Now that you have brushed up your basics, here are ten more frequently asked data modelling questions for your practice!
Explore our Popular Data Science Online Courses
11. Describe the process of data warehousing.
Data warehousing connects and manages raw data from heterogeneous sources. This data collection and analysis process allows business enterprises to get meaningful insights from varied locations in one place, which forms the core of Business Intelligence.
12. What are the key differences between a data mart and a data warehouse?
A data mart enables tactical decisions for business growth by focusing on a single business area and following a bottom-up model. On the other hand, a data warehouse facilitates strategic decision-making by emphasising multiple areas and data sources and adopting a top-down approach.
13. Mention the types of critical relationships found in data models.
Critical relationships can be categorised into:
- Identifying: Connects parent and child tables with a thick line. The child table’s reference column is a part of the primary key.
- Non-identifying: The tables are connected by a dotted line, signifying that the child table’s reference column is not a part of the primary key.
- Sef-recursive: A standalone column of the table is connected to the primary key in a recursive relationship.
14. What are some common errors that you encounter while modelling data?
It can get tricky to build broad data models. The chances of failure also increase when tables run higher than 200. It is also critical for the data modeller to have adequate workable knowledge of the business mission. Otherwise, the data models run the risk of going haywire.
Unnecessary surrogate keys pose another problem. They must not be used sparingly, but only when natural keys cannot fulfil the primary key’s role.
One can also encounter situations of inappropriate denormalisation where maintaining data redundancy can become a considerable challenge.
Top Data Science Skills to Learn to upskill
SL. No | Top Data Science Skills to Learn | |
1 |
Data Analysis Online Courses | Inferential Statistics Online Courses |
2 |
Hypothesis Testing Online Courses | Logistic Regression Online Courses |
3 |
Linear Regression Courses | Linear Algebra for Analysis Online Courses |
15. Discuss hierarchical DBMS. What are the drawbacks of this data model?
A hierarchical DBMS stores data in tree-like structures. The format uses the parent-child relationship where a parent may have many children, but a child can only have one parent.
The drawbacks of this model include:
- Lack of flexibility and adaptability to changing business needs;
- Issues in inter-departmental, inter-agency, and vertical communications;
- Problems of disunity in data.
16. Detail two types of data modelling techniques.
Entity-Relationship (E-R) and Unified Modeling Language (UML) are the two standard data modelling techniques.
E-R is used in software engineering to produce data models or diagrams of information systems. UML is a general-purpose language for database development and modelling that helps visualise the system design.
upGrad’s Exclusive Data Science Webinar for you –
Watch our Webinar on The Future of Consumer Data in an Open Data Economy
17. What is a junk dimension?
A junk dimension is born by combining low-cardinality attributes (indicators, booleans, or flag values) into one dimension. These values are removed from other tables and then grouped or ”junked” into an abstract dimension table, which is a method of initiating ‘Rapidly Changing Dimensions’ within data warehouses.
18. State some popular DBMS software.
MySQL, Oracle, Microsoft Access, dBase, SQLite, PostgreSQL, IBM DB2, and Microsoft SQL Server are some of the most-used DBMS tools in the modern-day software development arena.
Read our popular Data Science Articles
19. What are the advantages and disadvantages of using data modelling?
Pros of using data mining:
- Business data can be better managed by normalising and defining attributes.
- Data mining allows the integration of data across systems and reduces redundancy.
- It makes way for an efficient database design.
- It enables inter-departmental cooperation and teamwork.
- It allows easy access to data.
Cons of using data modelling:
- Data modelling can sometimes make the system more complex.
- It has a limited structural dependency.
20. Explain data mining and predictive modelling analytics.
Data mining is a multi-disciplinary skill. It involves applying knowledge from fields like Artificial Intelligence (AI), Machine Learning (ML), and Database Technologies. Here, practitioners are concerned with uncovering the mysteries of data and discovering previously unknown relationships.
Predictive modelling refers to testing and validating models that can predict specific outcomes. This process has several applications in AI, ML, and Statistics.
Career Insights for Aspiring Data Modelers
Whether you are looking for a fresh job, promotion, or career transition, upskilling in a relevant discipline can considerably improve your hiring chances.
You should consider checking out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
With this, we wind up this discussion on data modelling jobs and interviews. We are certain that the data mentioned above modelling interview questions and answers will help you clarify your problem areas and perform better in the placement process!
Frequently Asked Questions (FAQs)
1. How much does a Data Modeler make a year?
There are plenty of factors that would really affect the salary of any individual in the field of data modeling. On average, the salary of a data modeler is Rs. 12,00,000 per annum. It would depend a lot on the company that you are working with. Even if you are starting out as a data modeler, the lowest package is Rs. 600,000 per annum, while the highest package one can expect up to Rs. 20,00,000 per annum.
2. Is it difficult to crack a Data Modeling interview?
Data modeling is an emerging field with a huge demand in the market. On the other hand, the number of professionals who are proficient in data modeling is pretty less. The interview might seem a bit difficult if you haven’t prepared properly, but you can expect a decent interview with proper preparation.
Along with clearing the fundamentals of data modeling, you should also prefer going through some of the most frequently asked interview questions. This will make it much easier for you to answer the questions being asked in the interview as you already have an idea about the different questions being asked as well as the way of answering them.
3. What skills do I need to have to be a Data Modeler?
The skills required for becoming a data modeler are quite different from the ones needed for getting into systems administration or programming. Usually, these types of jobs demand technical skills, but the case is different over here. One needs to be well-versed on the logical side for becoming a data modeler. Some of the key skills that one needs to develop are:
1. Conceptual Design
2. Internal Communication
3. User Communication
4. Abstract Thinking
Even if you are not very proficient on the technical side, you can get a job as a data modeler if you can think abstractly and conceptually.