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 PIConvert your best B-School call with the fifth season of InsideIIM's highly-rated MBA Admissions Bootcamp - WATPI Edge! Over the last four years, we have a 94%+ success rate in our last four seasons. Enroll now! Sessions are ongoing!
Computer Science Interview Questions & Concepts for MBA WAT-PI 2025
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.
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.
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.
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.
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 ScienceMachine 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.
