50+ Computer Science Interview Questions & Concepts for MBA WAT-PI 2025

If you're preparing for the WAT (Written Ability Test) and PI (Personal Interview) rounds for MBA admissions or a job as a CSE graduate, having a solid understanding of computer science and data science fundamentals is crucial. This guide covers some of the most important concepts, followed by a full list of 66 questions that will help you prepare effectively.  In academics, we have previously covered: Mechanical Engineering, Marketing, Commerce, and Economics. Firstly we have given a general overview of important concepts. If you already have a strong hold on them scroll down to Actual CS Questions asked in MBA PI

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Computer Science Interview Questions & Concepts for MBA WAT-PI 2025

1. Understanding Algorithms and Data Structures

At the core of computer science lies algorithms and structured steps used to solve problems efficiently. Whether you're searching for a word in a document or sorting emails by date, algorithms play a vital role.  

Key data structures include:  

• Arrays Fixed-size collections of data stored sequentially.  
• Linked Lists Dynamic structures where each element points to the next.  
• Stacks and Queues Used in undo/redo functions and scheduling tasks.  
• Trees and Graphs Essential in AI, databases, and networking.  

When asked about these in an interview, try relating them to real-world examples—like how Google Maps uses graphs to find the shortest route.  

2. Object-Oriented Programming (OOP) 

If you've worked with Java, C++, or Python, you've likely used OOP. It’s a way of designing software using "objects" that mimic real-world entities.  

Key principles:  

• Encapsulation: Think of a capsule—you only see the outer shell (public methods), while the inner contents (private data) remain hidden.  
• Inheritance: Like a child inheriting traits from parents, one class can derive properties from another.  
• Polymorphism: The same function behaves differently in different situations—like a smartphone touchscreen responding to both tap and swipe

If you're asked about OOP in Python, you can mention how Python supports both OOP and functional programming, making it a flexible language.  

3. Operating System 

An Operating System (OS) is what makes a computer functional. Imagine trying to use a laptop without Windows, macOS, or Linux you wouldn’t even reach the desktop! 

Key concepts:  

• Process Management: How multiple applications run simultaneously.  
• Memory Management: Allocating RAM efficiently for active programs.  
• Deadlocks: When two programs are stuck waiting for each other, like two drivers blocking each other on a narrow road.  

4. Networking - How the Internet Works

Networking is all about how devices communicate. Some key terms:  

• TCP/IP Model: The backbone of the internet, ensuring reliable data transmission.  
• OSI Model: A conceptual model with 7 layers explaining how data flows in networks.  
• IPv4 vs IPv6: IPv6 is the newer internet addressing system, solving IPv4’s shortage of addresses.  
• Firewalls: Security barriers preventing unauthorized access to networks.  

If asked about networking, mention that cloud computing relies heavily on networking technologies such as VPNs, firewalls, and load balancing.

5. Databases - Storing and Managing Data

Every organization—from startups to tech giants—uses databases. Understanding SQL and NoSQL databases is key:  

• SQL (MySQL, PostgreSQL): Structured databases best for transactions.  
• NoSQL (MongoDB, Cassandra): Flexible and scalable, used in social media apps.  

Important terms:  

Normalization: Organizing data efficiently to avoid redundancy.  

ACID Properties: Ensuring database transactions are reliable.  

6. Machine Learning & Data Science

Machine Learning (ML) is transforming industries, from Netflix recommendations to fraud detection in banking 

Important ML concepts:  

• Supervised Learning: Learning from labeled data (e.g., spam detection).  
• Unsupervised Learning: Finding patterns in unlabeled data (e.g., customer segmentation).  
•  Overfitting & Underfitting: The balance between making a model too specific or too generalized.  
• Neural Networks & Deep Learning: How AI mimics the human brain.  

If asked why ML is important, you can mention its role in self-driving cars, medical diagnosis, and voice assistants like Alexa 

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Actual Questions Asked in MBA Interviews

1. What is an algorithm?  
2. Explain the difference between a compiler and an interpreter.  
3. What is recursion in programming?  
4. What are type 1 and type 2 errors?
5. Define object-oriented programming.  
6. What is data mining?  
7. Explain the concept of a stack and queue.    
8. Describe the concept of an array.  
9.  Explain the concept of pointers in C/C++.  
10. What is an operating system?  
11. Difference between Truncate and Delete in SQL  
12. What are your views on generative AI?
13. Describe the difference between paging and segmentation.  
14. Explain the role of an operating system in memory management.  
15. Explain the difference between SQL and NoSQL databases.  
16.  Describe the concept of normalization in databases.  
17. What are the ACID properties in databases?  
18. 7 OSI Layers
19. What is TCP/IP?  
20. Explain the difference between a router and a modem to a layman.  
21. What is a firewall?    
22. Explain the software development life cycle (SDLC).  
23. What is agile methodology?  
24. Describe the concept of version control.  
25. Differentiate between Java and .NET Technologies.
26. What is unit testing in software development?       
27. What are the key features of Java?  
28. Explain the concept of inheritance in Python.  
29. What is polymorphism in programming languages?  
30. Describe the use of lambda functions in Python.  
31. What is client-side architecture? 
32. Explain the concept of binary search.  
33. What is a hash table?  
34. Describe the quicksort algorithm.  
35. Define machine learning and its importance in today’s world.  
36. Write a C++ code to find the smallest of 10 numbers  
37. Explain the concept of overfitting and underfitting in ML.  
38. Describe the difference between classification and regression.  
39. 4 pillars of OOP
40. How does a decision tree algorithm work?  
41. Explain cloud computing and its challenges.
42. Data Science vs Traditional Data Analysis
43. IPv4 vs IPv6
44. Give an algorithm for the divisibility rule of 9.
45. What is data visualization and which tools are commonly used for it?
46. Define graph algorithm.
47. Describe the use of support vector machines (SVM) in machine learning
48. Difference between runtime and compilation error.
49. What is a data warehouse and how is it different from a database?  
50. Define Bit. 
51. Difference between alpha and beta testing.

Tanisha Sinha writes this article. Connect with the author on LinkedIn. 

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