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Top Interview Questions
Computer Science: An Overview
Computer Science (CS) is the study of computers and computational systems. Unlike electrical and computer engineers, computer scientists focus primarily on software and software systems; this includes their theory, design, development, and application. As a discipline, computer science combines both theoretical foundations and practical techniques to solve problems using computers. It plays a central role in the modern world, impacting fields ranging from business and healthcare to entertainment, artificial intelligence, and cybersecurity.
History and Evolution of Computer Science
The origins of computer science can be traced back to the development of early computational devices and theoretical models. In the 19th century, Charles Babbage designed the Analytical Engine, a mechanical general-purpose computer, while Ada Lovelace wrote what is considered the first algorithm intended to be processed by a machine. However, the formal development of computer science as a discipline began in the 20th century.
During World War II, the need for fast calculations led to the creation of early electronic computers like the ENIAC (Electronic Numerical Integrator and Computer). In the 1950s, programming languages such as Fortran and COBOL emerged, allowing computers to be used for scientific and business applications. The 1960s and 1970s saw the rise of algorithms, data structures, and operating systems, forming the core theoretical foundation of modern computer science. Over time, the discipline expanded to include areas such as artificial intelligence, database systems, networking, human-computer interaction, and cybersecurity.
Core Areas of Computer Science
Computer science is a broad field, encompassing various areas of study. The key areas include:
Algorithms and Data Structures:
Algorithms are step-by-step procedures for solving problems, while data structures are ways of organizing and storing data efficiently. Together, they form the backbone of computer science, influencing the efficiency and effectiveness of software and applications. Understanding algorithms and data structures is essential for solving complex computational problems and is a key focus in software development and research.
Programming Languages:
Programming languages are formal languages used to write software. Popular languages include Python, Java, C++, and JavaScript. Each language has its strengths and applications. For instance, Python is favored in data science and artificial intelligence, while C++ is often used for system-level programming and high-performance applications. Learning programming languages is fundamental for translating computational problems into executable programs.
Software Engineering:
Software engineering involves the systematic design, development, testing, and maintenance of software systems. It emphasizes principles such as modularity, scalability, reliability, and maintainability. Techniques like agile development, version control, and continuous integration have transformed software engineering into a structured and collaborative discipline, allowing teams to build complex systems efficiently.
Computer Architecture and Systems:
This area deals with the internal organization of computers, including processors, memory, storage, and networking components. Understanding computer architecture helps optimize software performance and develop efficient systems. Additionally, operating systems play a crucial role by managing hardware resources and providing a platform for application execution.
Artificial Intelligence (AI) and Machine Learning (ML):
AI is the study of creating systems that can perform tasks requiring human intelligence, such as reasoning, learning, problem-solving, and decision-making. Machine learning, a subset of AI, involves developing algorithms that enable computers to learn from data. Applications of AI and ML range from voice assistants and autonomous vehicles to medical diagnosis and financial predictions.
Databases and Information Systems:
Databases store, organize, and manage large amounts of data. Database management systems (DBMS) like MySQL, Oracle, and MongoDB provide tools for efficient data storage, retrieval, and manipulation. Information systems integrate databases with applications to support business processes, decision-making, and analytics.
Networking and Communication:
Computer networks connect devices to share information and resources. Networking involves protocols, architectures, and technologies that ensure reliable and secure data communication. The Internet, cloud computing, and distributed systems are essential components of modern networking, enabling global connectivity and large-scale applications.
Cybersecurity:
With the increasing reliance on digital systems, protecting information and systems from cyber threats has become critical. Cybersecurity involves implementing measures to prevent unauthorized access, data breaches, and cyberattacks. Techniques include encryption, firewalls, intrusion detection systems, and ethical hacking.
Human-Computer Interaction (HCI):
HCI focuses on designing user-friendly interfaces and interactions between humans and computers. It involves understanding user behavior, ergonomics, and usability principles. Applications include web design, mobile apps, virtual reality, and gaming.
Applications of Computer Science
The applications of computer science are vast and touch almost every aspect of modern life. Some notable areas include:
Healthcare: Computer science enables advanced medical imaging, patient data management, telemedicine, and AI-based diagnostic tools. Machine learning models can predict disease outbreaks, personalize treatments, and analyze genetic data.
Business and Finance: Companies use computer systems for automation, data analytics, financial modeling, and e-commerce. Algorithms optimize supply chains, detect fraud, and provide personalized customer experiences.
Education: Computer-based learning platforms, virtual classrooms, and adaptive learning systems enhance education quality. Artificial intelligence helps provide personalized learning experiences and assess student performance.
Entertainment: Computer science drives video games, animation, virtual reality, and digital media production. Graphics algorithms, AI-based character behaviors, and streaming technologies have transformed the entertainment industry.
Science and Research: High-performance computing, simulations, and data analysis tools enable researchers to study complex phenomena in physics, biology, climate science, and engineering.
Transportation: Autonomous vehicles, traffic management systems, and logistics planning rely heavily on algorithms, sensors, and AI.
Importance of Computer Science in Modern Society
Computer science has become a cornerstone of technological progress and economic growth. Its importance can be highlighted in several ways:
Problem-Solving: Computer science equips individuals with analytical and computational skills to solve real-world problems efficiently. From optimizing business operations to modeling scientific phenomena, it provides tools and methods for innovation.
Job Opportunities: The demand for skilled computer scientists continues to rise globally. Careers range from software developers, data scientists, and AI specialists to cybersecurity analysts and systems architects. Knowledge of computer science enhances employability across industries.
Innovation and Research: Advances in computer science drive innovation in artificial intelligence, robotics, cloud computing, quantum computing, and biotechnology. Research in algorithms, machine learning, and human-computer interaction pushes the boundaries of what technology can achieve.
Digital Transformation: Computer science enables the digitization of industries, governments, and daily life. It facilitates the creation of smart cities, digital healthcare systems, and automated financial services.
Future of Computer Science
The future of computer science is promising, with emerging technologies poised to transform society further. Key trends include:
Quantum Computing: Leveraging the principles of quantum mechanics, quantum computers have the potential to solve problems in seconds that would take classical computers millennia.
Artificial Intelligence: AI continues to evolve, with advancements in deep learning, natural language processing, and autonomous systems reshaping industries and daily life.
Internet of Things (IoT): Connecting billions of devices to collect and analyze data will revolutionize smart homes, industrial automation, and healthcare monitoring.
Cybersecurity and Privacy: As data becomes more integral, developing robust security and privacy solutions will be crucial to protect information and maintain trust.
Sustainable Computing: Green computing practices aim to reduce the environmental impact of data centers, computational devices, and energy-intensive processes.
Answer:
Computer Science is the study of computers and computational systems. It focuses on the theory, design, development, and application of software and systems. It includes topics like programming, algorithms, data structures, databases, networking, artificial intelligence, and more.
Answer:
An algorithm is a step-by-step procedure or formula for solving a problem. It’s a sequence of instructions that tells the computer how to perform a specific task.
Example: Sorting numbers in ascending order is done using sorting algorithms like Bubble Sort, Quick Sort, or Merge Sort.
Answer:
| Feature | Compiler | Interpreter |
|---|---|---|
| Converts code | Converts entire source code to machine code | Converts and executes code line by line |
| Speed | Faster execution | Slower execution |
| Error detection | Shows errors after compilation | Shows errors line by line |
| Example | C, C++ | Python, Ruby |
Answer:
A data structure is a way of organizing and storing data to perform operations efficiently. Common data structures include:
Array: Collection of elements of same type stored in contiguous memory.
Linked List: Sequence of nodes, each containing data and a pointer to the next node.
Stack: Last-In-First-Out (LIFO) structure.
Queue: First-In-First-Out (FIFO) structure.
Tree/Graph: Hierarchical and network-based structures.
Answer:
OOP is a programming paradigm based on objects that contain data (attributes) and methods (functions).
Key concepts:
Class: Blueprint of an object.
Object: Instance of a class.
Inheritance: Child class inherits properties of parent class.
Polymorphism: Ability to take multiple forms (overloading and overriding).
Encapsulation: Wrapping data and methods in a single unit.
Abstraction: Hiding internal details and showing functionality only.
Answer:
| Feature | Stack | Queue |
|---|---|---|
| Principle | LIFO (Last In First Out) | FIFO (First In First Out) |
| Operations | push(), pop() | enqueue(), dequeue() |
| Example | Undo functionality | Print Queue |
Answer:
A database is an organized collection of data, stored and accessed electronically. Databases are managed using DBMS (Database Management System).
Types of Databases:
Relational Database: Data is stored in tables (e.g., MySQL, Oracle).
Non-Relational Database: Data is stored as key-value pairs, documents, etc. (e.g., MongoDB, Redis).
Answer:
SQL (Structured Query Language) is used to communicate with a database to perform operations such as:
SELECT: Retrieve data
INSERT: Add data
UPDATE: Modify data
DELETE: Remove data
Answer:
| Feature | HTTP | HTTPS |
|---|---|---|
| Security | Not secure | Secure (uses SSL/TLS) |
| Port | 80 | 443 |
| Data | Sent in plaintext | Encrypted |
| Use | Simple websites | Banking, e-commerce |
Answer:
OSI (Open Systems Interconnection) model is a conceptual framework used to understand network interactions. It has 7 layers:
Physical – Transmission of raw bits (Cables, Hubs).
Data Link – Error detection, MAC addressing (Switches).
Network – Routing and logical addressing (IP).
Transport – Reliable transmission (TCP/UDP).
Session – Manages sessions between applications.
Presentation – Data translation, encryption, compression.
Application – End-user services (HTTP, FTP).
Answer:
| Feature | Process | Thread |
|---|---|---|
| Definition | Program in execution | Lightweight subprocess within a process |
| Memory | Separate memory space | Shares memory of process |
| Communication | Inter-process communication needed | Direct communication using shared memory |
| Overhead | Higher | Lower |
Answer:
Big O Notation measures the efficiency of an algorithm in terms of time complexity or space complexity.
O(1): Constant time
O(n): Linear time
O(n²): Quadratic time
O(log n): Logarithmic time
Answer:
Recursion is a process where a function calls itself directly or indirectly to solve a smaller version of the problem.
Example: Factorial of n:
int factorial(int n) {
if(n == 0) return 1;
else return n * factorial(n-1);
}
Answer:
A programming language is a formal set of instructions used to communicate with a computer.
Types:
Low-Level Language: Close to machine code (Assembly).
High-Level Language: Easy to read/write (Python, Java, C++).
Answer:
| Feature | Static | Dynamic |
|---|---|---|
| Allocation Time | Compile-time | Run-time |
| Size | Fixed | Flexible |
| Example | Arrays | Linked List, malloc() in C |
Answer:
Git is a version control system used to track changes in source code during software development.
Key Commands:
git init – Initialize repository
git clone – Copy repository
git add – Stage changes
git commit – Save changes
git push – Upload changes to remote repository
Answer:
Cloud Computing provides on-demand computing resources (servers, storage, databases, networking) over the internet.
Types:
IaaS (Infrastructure as a Service)
PaaS (Platform as a Service)
SaaS (Software as a Service)
Answer:
Machine Learning is a subset of AI where systems learn from data to make predictions without being explicitly programmed.
Types:
Supervised Learning – Labeled data
Unsupervised Learning – Unlabeled data
Reinforcement Learning – Learning via rewards
Answer:
| Feature | AI | ML | DL |
|---|---|---|---|
| Definition | Broad concept of machines simulating human intelligence | Subset of AI using data to learn | Subset of ML using neural networks |
| Example | Chess-playing robot | Predicting house prices | Image recognition |
Answer:
| Feature | GET | POST |
|---|---|---|
| Data visibility | Appended to URL | Hidden in request body |
| Use | Fetch data | Send data |
| Security | Less secure | More secure |
| Caching | Can be cached | Cannot be cached |
Answer:
A linked list is a linear data structure where each element (node) contains:
Data – The value stored
Pointer – Reference to the next node
Types:
Singly Linked List – Each node points to the next node.
Doubly Linked List – Each node points to the next and previous node.
Circular Linked List – Last node points back to the first node.
Example:
struct Node {
int data;
struct Node* next;
};
Answer:
A binary tree is a hierarchical data structure where each node has at most 2 children (left and right).
Special Types:
Binary Search Tree (BST) – Left child < root < right child
Complete Binary Tree – All levels filled except possibly last
Full Binary Tree – Every node has 0 or 2 children
Answer:
Hashing is a technique to store and retrieve data efficiently using a hash function. It converts a key into an index in a hash table.
Example:
If key = 45, hash function = key % 10 → Index = 5.
Collision Handling:
Chaining – Linked list at each index
Open Addressing – Find next empty slot
Answer:
Deadlock occurs when two or more processes wait for each other indefinitely.
Necessary Conditions:
Mutual exclusion
Hold and wait
No preemption
Circular wait
Prevention: Avoid at least one of the above conditions.
Answer:
Paging is a memory management scheme that eliminates fragmentation by dividing memory into fixed-size blocks called pages (logical memory) and frames (physical memory).
Example: Page size = 4KB → Process with 12KB → 3 pages
| Feature | Process | Program |
|---|---|---|
| Definition | A program in execution | A set of instructions |
| State | Active | Passive |
| Memory | Allocated | Not allocated |
| Example | Running Chrome | Chrome.exe file |
Answer:
Virtual memory allows a system to use more memory than physically available by using a portion of the hard disk as RAM.
Benefits:
Multitasking
Running large programs
Efficient memory utilization
Answer:
Normalization organizes a database to reduce redundancy and improve integrity.
Normal Forms:
1NF – No repeating groups
2NF – No partial dependency
3NF – No transitive dependency
BCNF – Stronger form of 3NF
Example:
Before 1NF: Multiple phone numbers in one column.
After 1NF: Separate rows for each number.
Answer:
Primary Key: Unique identifier for a table row.
Foreign Key: Column that refers to a primary key in another table.
Example:
StudentID (Primary Key) → Student table
CourseID (Foreign Key) → Enroll table
Answer:
TCP (Transmission Control Protocol): Connection-oriented, reliable, ordered delivery
UDP (User Datagram Protocol): Connectionless, faster, no guarantee of delivery
| Feature | TCP | UDP |
|---|---|---|
| Reliability | Yes | No |
| Speed | Slower | Faster |
| Use | File transfer, emails | Streaming, gaming |
Answer:
| OSI | TCP/IP |
|---|---|
| 7 layers | 4 layers |
| Theoretical model | Practical model |
| Layered architecture | Simplified architecture |
| Application, Presentation, Session, Transport, Network, Data Link, Physical | Application, Transport, Internet, Network Access |
| Feature | Stack | Heap |
|---|---|---|
| Allocation | LIFO | Dynamic |
| Lifetime | Temporary | Until manually freed |
| Size | Smaller | Larger |
| Use | Function calls | Dynamic objects |
Answer:
Polymorphism allows objects of different types to be treated as objects of a common base type.
Types:
Compile-time (Static): Method overloading, operator overloading
Run-time (Dynamic): Method overriding
Example:
class Shape {
void draw() { System.out.println("Drawing shape"); }
}
class Circle extends Shape {
void draw() { System.out.println("Drawing circle"); }
}
| Feature | Overloading | Overriding |
|---|---|---|
| Compile/Run | Compile-time | Run-time |
| Method name | Same | Same |
| Parameters | Different | Same |
| Return type | Can be different | Must be same or covariant |
| Feature | Recursion | Iteration |
|---|---|---|
| Definition | Function calls itself | Looping (for, while) |
| Memory | Uses stack | Less memory |
| Example | Factorial using recursion | Factorial using loop |
Answer:
A graph is a data structure consisting of vertices (nodes) and edges (connections).
Types:
Directed / Undirected
Weighted / Unweighted
Cyclic / Acyclic
Example:
Social network → users as nodes, friendships as edges
| Feature | BFS (Breadth First Search) | DFS (Depth First Search) |
|---|---|---|
| Approach | Level-wise | Depth-wise |
| Data Structure | Queue | Stack/Recursion |
| Use | Shortest path | Pathfinding, solving puzzles |
| Feature | Static Binding | Dynamic Binding |
|---|---|---|
| Time | Compile-time | Run-time |
| Method | Private, final | Overridden methods |
| Example | Method overloading | Method overriding |
Answer:
Exception handling manages runtime errors to prevent program crashes.
Common keywords (Java/C#):
try – Code that may throw exception
catch – Handles exception
finally – Code executed regardless of exception
throw – Throws exception manually
Example (Java):
try {
int result = 10 / 0;
} catch(ArithmeticException e) {
System.out.println("Cannot divide by zero");
}
| Feature | Array | Linked List |
|---|---|---|
| Memory | Contiguous | Non-contiguous |
| Size | Fixed | Dynamic |
| Access | O(1) | O(n) |
| Insertion/Deletion | Costly | Efficient |
Answer:
A queue is a linear data structure that follows FIFO (First In First Out) principle.
Types of Queue:
Simple Queue – Linear queue, standard FIFO.
Circular Queue – Last position connects back to the first.
Priority Queue – Elements are processed based on priority.
Deque (Double-Ended Queue) – Insert/delete from both ends.
Example in Python:
from collections import deque
q = deque([1,2,3])
q.append(4) # enqueue
q.popleft() # dequeue
| Feature | Linear | Non-linear |
|---|---|---|
| Data arrangement | Sequential | Hierarchical/Graph |
| Examples | Array, Linked List, Stack, Queue | Tree, Graph |
| Traversal | Simple | Complex |
Answer:
Heap is a complete binary tree used for priority-based operations.
Max Heap – Parent ≥ Children
Min Heap – Parent ≤ Children
Use: Priority queues, Heap sort, Memory management.
Answer:
Binary search finds an element in a sorted array by repeatedly dividing the search interval in half.
Algorithm:
Find middle element.
If key = middle, return index.
If key < middle, search left half.
Else, search right half.
Complexity: O(log n)
| Feature | Linear Search | Binary Search |
|---|---|---|
| Array must be sorted | No | Yes |
| Complexity | O(n) | O(log n) |
| Approach | Sequential | Divide and Conquer |
| Use | Small datasets | Large datasets |
Answer:
Graph traversal is visiting all nodes of a graph in a systematic way.
Methods:
BFS (Breadth First Search) – Level order using queue
DFS (Depth First Search) – Depthwise using recursion/stack
Applications: Pathfinding, network analysis.
Answer:
Virtualization allows multiple virtual machines (VMs) to run on a single physical machine using a hypervisor.
Benefits:
Resource optimization
Isolation
Easier backup & migration
Example: VMware, Hyper-V
Answer:
SQL Injection is a security vulnerability where attackers manipulate SQL queries to access unauthorized data.
Example:
SELECT * FROM users WHERE username = 'admin' OR 1=1;
Prevention: Use prepared statements, parameterized queries.
Answer:
Indexing is a technique to improve database query performance by creating a data structure that allows fast search.
Types:
Primary Index – On primary key
Secondary Index – On non-primary columns
Clustered Index – Data physically ordered
| Feature | SQL | NoSQL |
|---|---|---|
| Structure | Tables (Relational) | Documents, Key-Value, Graph |
| Schema | Fixed | Flexible |
| Transactions | ACID | BASE |
| Example | MySQL, Oracle | MongoDB, Cassandra |
Answer:
Deadlock: Two or more processes wait indefinitely for resources held by each other.
Starvation: A process waits indefinitely because higher-priority processes keep executing.
Difference: Deadlock stops all processes; starvation affects only some processes.
Answer:
Semaphore is a synchronization tool used to control access to shared resources in concurrent systems.
Types:
Binary Semaphore: 0 or 1 (Mutex)
Counting Semaphore: Integer value representing available resources
Example:
wait(semaphore);
critical_section();
signal(semaphore);
| Feature | Process | Thread |
|---|---|---|
| Definition | Program in execution | Lightweight process within a process |
| Memory | Separate | Shared memory |
| Creation | Heavyweight | Lightweight |
| Context Switch | Expensive | Cheap |
Answer:
Public Cloud: Services available to general public (AWS, Azure).
Private Cloud: Exclusive to a company.
Hybrid Cloud: Combination of Public & Private Cloud.
Community Cloud: Shared by multiple organizations.
| Feature | GET | POST |
|---|---|---|
| Data | URL appended | Request body |
| Security | Low | High |
| Cache | Can be cached | Cannot be cached |
| Idempotent | Yes | No |
Answer:
REST (Representational State Transfer) is an architectural style for web services.
Key Points:
Stateless communication
CRUD operations mapped to HTTP methods:
GET → Read
POST → Create
PUT → Update
DELETE → Delete
Uses JSON/XML for data exchange
| Feature | HTTP | HTTPS |
|---|---|---|
| Security | Not secure | Secure (SSL/TLS) |
| Port | 80 | 443 |
| Encryption | No | Yes |
Answer:
Machine Learning is a subset of AI where systems learn patterns from data without explicit programming.
Types:
Supervised Learning – Labeled data (Regression, Classification)
Unsupervised Learning – Unlabeled data (Clustering, Association)
Reinforcement Learning – Learning via rewards/punishments
| Feature | AI | ML | DL |
|---|---|---|---|
| Definition | Intelligence simulation | Learn from data | Learn using neural networks |
| Complexity | Simple | Moderate | High |
| Example | Chess AI | Predict stock prices | Image recognition |
Answer:
IDE (Integrated Development Environment) is a software application that provides:
Code editor
Compiler/Interpreter
Debugger
Build automation
Example: Eclipse, Visual Studio, PyCharm
Answer:
| Feature | Stack | Heap |
|---|---|---|
| Memory Allocation | Static | Dynamic |
| Lifetime | Function execution time | Until manually freed or GC |
| Access | Fast | Slower |
| Size | Limited | Large |
| Use Case | Local variables, function calls | Dynamic objects |
| Example | int x = 10; inside a function |
new Object() in Java |
Answer:
Deadlock occurs when two or more processes wait indefinitely for resources held by each other.
Necessary Conditions:
Mutual exclusion
Hold and wait
No preemption
Circular wait
Prevention Techniques:
Deadlock Prevention: Ensure one of the above conditions never holds.
Deadlock Avoidance: Use algorithms like Banker’s algorithm to allocate resources safely.
Deadlock Detection: Detect and terminate/restart processes when deadlock occurs.
Answer:
Virtual memory allows the system to run programs larger than physical RAM using a portion of the hard disk.
Paging: Memory is divided into fixed-size pages (logical memory) and frames (physical memory). Pages are mapped to frames using a page table.
Advantages:
Efficient memory utilization
Supports multitasking
Eliminates external fragmentation
Answer:
Normalization is a process of organizing database tables to reduce redundancy and improve data integrity.
Normal Forms:
1NF: No repeating groups
2NF: No partial dependency
3NF: No transitive dependency
BCNF: Stronger version of 3NF
Example:
Before 1NF:
| Student | Courses |
|---|---|
| John | Math, Physics |
After 1NF:
| Student | Course |
|---|---|
| John | Math |
| John | Physics |
| Feature | TCP | UDP |
|---|---|---|
| Connection | Connection-oriented | Connectionless |
| Reliability | Reliable | Unreliable |
| Flow Control | Yes | No |
| Use Case | File transfer, emails | Video streaming, gaming |
Answer:
OSI Model (7 Layers):
Physical – Bits transmission
Data Link – MAC addressing, error detection
Network – IP addressing, routing
Transport – TCP/UDP
Session – Session management
Presentation – Encryption/Translation
Application – HTTP, FTP
TCP/IP Model (4 Layers):
Network Interface – Physical + Data Link
Internet – IP addressing
Transport – TCP/UDP
Application – HTTP, FTP, DNS
Key Difference: OSI is theoretical; TCP/IP is practical.
Answer:
Polymorphism allows objects to take multiple forms.
Types:
Compile-time (Static): Method overloading, operator overloading
Run-time (Dynamic): Method overriding
Example (Java):
class Shape {
void draw() { System.out.println("Drawing shape"); }
}
class Circle extends Shape {
void draw() { System.out.println("Drawing circle"); }
}
At runtime, Shape s = new Circle(); s.draw(); will call Circle’s method.
| Feature | Overloading | Overriding |
|---|---|---|
| Compile/Run | Compile-time | Run-time |
| Parameters | Different | Same |
| Return Type | Can differ | Must be same/covariant |
| Example | int sum(int a, int b) & float sum(float a, float b) |
Child class overrides parent method |
Answer:
| Feature | REST | SOAP |
|---|---|---|
| Protocol | HTTP | XML-based protocol |
| Data Format | JSON, XML | XML |
| State | Stateless | Can be Stateful |
| Complexity | Simple | Complex |
| Use | Modern web apps | Enterprise apps |
Answer:
Machine Learning (ML) is a subset of AI where systems learn patterns from data without explicit programming.
Types:
Supervised Learning: Labeled data → Regression, Classification
Unsupervised Learning: Unlabeled data → Clustering, Association
Reinforcement Learning: Learn via rewards/punishments → Games, Robotics
Answer:
Public Cloud: Shared resources, accessible to all (AWS, Azure)
Private Cloud: Dedicated resources, internal use
Hybrid Cloud: Combination of Public & Private
Community Cloud: Shared by specific organizations
| Feature | SQL | NoSQL |
|---|---|---|
| Structure | Relational tables | Key-value, Document, Graph |
| Schema | Fixed | Flexible |
| ACID | Yes | BASE (Eventually consistent) |
| Example | MySQL, Oracle | MongoDB, Cassandra |
Answer:
Indexing improves query performance by creating a data structure (B-Tree, Hash Table) that allows fast search.
Types:
Primary Index – On primary key
Secondary Index – On non-primary key
Clustered Index – Data physically ordered
Answer:
BST is a binary tree where left child < root < right child.
Operations:
Search – O(log n) on average, O(n) worst-case (skewed)
Insert – O(log n)
Delete – O(log n)
Example:
15
/ \
10 20
/ \ / \
8 12 17 25
| Feature | Process | Thread |
|---|---|---|
| Definition | Program in execution | Lightweight process inside a process |
| Memory | Separate | Shared |
| Creation | Heavy | Light |
| Context Switch | Expensive | Cheap |
Answer:
A graph is a collection of nodes (vertices) connected by edges.
Types:
Directed / Undirected
Weighted / Unweighted
Cyclic / Acyclic
Special Graphs: Tree, DAG
Applications: Social networks, GPS navigation, recommendation systems
| Feature | BFS | DFS |
|---|---|---|
| Traversal | Level by level | Depth first |
| Data Structure | Queue | Stack / Recursion |
| Use Case | Shortest path | Topological sort, puzzles |
Answer:
Caching stores frequently used data in faster storage to improve performance.
Types:
Memory Cache – RAM
Disk Cache – Hard drive
Browser Cache – Web content
Database Cache – Query results
Answer:
Load balancing distributes incoming network traffic across multiple servers to ensure high availability and reliability.
Types:
Round Robin – Sequential distribution
Least Connections – Server with fewest connections
IP Hash – Based on client IP
Answer:
Microservices architecture divides an application into small, independent services that communicate via APIs.
Advantages:
Scalability
Independent deployment
Fault isolation
Example: E-commerce app → Separate services for payment, inventory, user management
Answer:
A Hash Table is a data structure that stores key-value pairs and uses a hash function to compute an index for each key.
Collision: When two keys hash to the same index.
Collision Handling Techniques:
Chaining: Store multiple elements at the same index using a linked list.
Open Addressing: Find the next available slot using techniques like Linear Probing, Quadratic Probing, or Double Hashing.
Example:
hash_table = [None]*10
key = 15
index = key % 10 # Hash Function
Answer:
A Trie is a tree-like data structure used to store strings efficiently. Each node represents a character.
Applications:
Autocomplete
Spell checking
IP routing (Longest prefix match)
Example: Words “cat”, “cap”, “can” share the prefix “ca”.
Answer:
Caching policies used to decide which items to evict when the cache is full:
LRU (Least Recently Used): Removes the item that was used the longest time ago.
LFU (Least Frequently Used): Removes the item with the lowest access frequency.
Implementation: HashMap + Doubly Linked List for O(1) access (LRU).
| Feature | B-Tree | B+ Tree |
|---|---|---|
| Data Storage | Internal & Leaf nodes | Leaf nodes only |
| Traversal | All nodes | Leaf nodes (linked list for range queries) |
| Use Case | Indexed DB search | File systems, database indexing |
| Complexity | O(log n) | O(log n) |
Answer:
A Red-Black Tree is a self-balancing binary search tree with the following properties:
Each node is red or black.
Root is always black.
Red nodes cannot have red children (no two reds in a row).
Every path from a node to its leaf has the same number of black nodes.
Use Case: Balanced search tree for efficient insertion/deletion.
| Feature | Merge Sort | Quick Sort |
|---|---|---|
| Approach | Divide and Conquer | Divide and Conquer |
| Worst-case | O(n log n) | O(n²) |
| Best-case | O(n log n) | O(n log n) |
| Space Complexity | O(n) | O(log n) (in-place) |
| Stability | Yes | No |
Answer:
Dynamic Programming (DP) is a technique to solve problems by breaking them into subproblems and storing results to avoid recomputation.
Example: Fibonacci sequence
def fib(n):
dp = [0]*(n+1)
dp[0], dp[1] = 0, 1
for i in range(2, n+1):
dp[i] = dp[i-1] + dp[i-2]
return dp[n]
Applications:
Knapsack problem
Longest Common Subsequence
Matrix Chain Multiplication
Answer:
ACID ensures reliable transaction processing:
Atomicity: All or nothing
Consistency: DB remains in valid state
Isolation: Transactions don’t interfere
Durability: Committed changes persist
Example: Banking transfer ensures debit and credit are atomic.
Answer:
CAP Theorem states that a distributed system can have only two out of three guarantees at the same time:
Consistency: Every read receives the latest write
Availability: Every request gets a response
Partition Tolerance: System works despite network partitions
Example: NoSQL systems like MongoDB, Cassandra follow CAP trade-offs.
| Feature | Synchronous | Asynchronous |
|---|---|---|
| Execution | Waits for response | Does not wait |
| Use Case | RPC, Function Calls | Message Queues, Event-driven systems |
| Efficiency | Can be slower | More efficient |
Answer:
In Event-Driven Architecture, components communicate via events instead of direct calls.
Advantages:
Decoupled components
Scalable systems
Real-time processing
Example: Kafka, RabbitMQ for messaging.
| Feature | REST | GraphQL |
|---|---|---|
| Data Fetch | Fixed endpoints | Flexible queries |
| Over-fetching | Possible | Avoided |
| Versioning | Required | Not required |
| Example | /users/1 |
{ user(id:1) {name, email} } |
Answer:
OAuth 2.0 is an authorization framework that allows third-party apps to access user resources without sharing credentials.
Roles:
Resource Owner – User
Client – Application requesting access
Authorization Server – Issues tokens
Resource Server – Hosts resources
Example: Login with Google/Facebook.
Answer:
Docker is a containerization platform to run applications in lightweight isolated environments.
Benefits:
Portability
Consistency across environments
Efficient resource utilization
Commands:
docker build – Build image
docker run – Run container
docker ps – List running containers
Answer:
Kubernetes is a container orchestration platform to manage deployment, scaling, and management of containerized applications.
Components:
Pod – Smallest deployable unit
Node – Worker machine
Cluster – Master + Nodes
Service – Networking abstraction
Use Case: Scalable microservices deployment.
Answer:
| Feature | OAuth | JWT |
|---|---|---|
| Purpose | Authorization framework | Token format |
| State | Server maintains state | Stateless |
| Example | Login with Google | Access tokens in APIs |
| Feature | RabbitMQ | Kafka |
|---|---|---|
| Type | Message Broker | Distributed Streaming |
| Message Model | Queue-based | Log-based |
| Persistence | Optional | Durable by default |
| Use Case | Real-time messaging | High-throughput event streaming |
Answer:
Capacity planning is predicting resource requirements to ensure applications scale efficiently without overprovisioning.
Steps:
Measure current usage
Forecast growth
Allocate resources dynamically (autoscaling in AWS/Azure)
Answer:
CI (Continuous Integration): Developers merge code frequently → Automated builds/tests
CD (Continuous Deployment/Delivery): Code changes automatically deployed to production or staging
Tools: Jenkins, GitLab CI/CD, CircleCI
| Feature | Monolithic | Microservices |
|---|---|---|
| Structure | Single codebase | Independent services |
| Scalability | Hard | Easy |
| Deployment | Single | Independent |
| Fault Isolation | Poor | Good |
Answer:
Sharding is a database partitioning technique that splits a large database into smaller, faster, and more manageable pieces called shards.
Types:
Horizontal Sharding: Rows are distributed across multiple databases.
Vertical Sharding: Columns are split into different tables or databases.
Use Case: High-scale systems like Twitter, Amazon, where one database cannot handle all traffic.
Answer:
Consistent hashing is a technique to distribute keys across nodes in a way that minimizes re-distribution when nodes are added/removed.
Use Case: Distributed caching systems like Redis Cluster, Memcached.
Example: If a node is removed, only the keys in that node need redistribution, not the entire hash table.
| Feature | Horizontal Scaling | Vertical Scaling |
|---|---|---|
| Method | Add more machines | Increase resources of a single machine |
| Cost | Moderate to high | High upfront |
| Fault Tolerance | High | Low |
| Example | Load-balanced web servers | Upgrading RAM/CPU |
Answer:
CAP Theorem: Distributed systems can guarantee only 2 out of 3:
Consistency: All nodes see the same data at the same time.
Availability: Every request receives a response.
Partition Tolerance: System continues to work despite network failure.
Example:
MongoDB: AP (Availability + Partition-tolerant)
HBase: CP (Consistency + Partition-tolerant)
Answer:
CQRS is an architecture pattern that separates read operations from write operations.
Advantages:
Optimized reads and writes separately
Supports event sourcing
Easier scaling
Use Case: High-traffic e-commerce platforms where reads are frequent.
| Feature | Strong Consistency | Eventual Consistency |
|---|---|---|
| Guarantee | Immediate consistency across nodes | Data may take time to propagate |
| Latency | Higher | Lower |
| Use Case | Banking systems | Social media feeds |
ACID (DB transactions): Atomicity, Consistency, Isolation, Durability – used in SQL databases.
BASE (NoSQL): Basically Available, Soft state, Eventually consistent – used in NoSQL systems.
CAP (Distributed systems): Consistency, Availability, Partition tolerance – trade-offs for distributed DBs.
Example: SQL DB → ACID; MongoDB → BASE; Cassandra → AP (CAP).
Answer:
Rate limiting restricts the number of requests a client can make in a given time to prevent abuse or overload.
Techniques:
Fixed Window Counter
Sliding Window Log
Token Bucket / Leaky Bucket
Use Case: Preventing API abuse like login attempts or public API usage.
| Feature | OAuth | JWT |
|---|---|---|
| Purpose | Authorization | Token format for authentication |
| State | Server maintains | Stateless |
| Use | Login via third-party | API token exchange |
| Example | Google/Facebook login | Access token in REST API |
| Feature | Synchronous | Asynchronous |
|---|---|---|
| Communication | Waits for response | Does not wait |
| Throughput | Low | High |
| Use Case | RPC calls | Event-driven architectures |
| Example | HTTP request-response | Kafka, RabbitMQ messaging |
Answer:
EDA decouples components using events instead of direct calls.
Advantages:
Loose coupling
Real-time updates
Scalability
Example: E-commerce: OrderPlaced event triggers Payment and Inventory services asynchronously.
| Technique | Description |
|---|---|
| Round Robin | Requests distributed sequentially |
| Least Connections | Request sent to server with fewest active connections |
| IP Hash | Client IP determines server |
| Weighted Load Balancing | Servers assigned weights based on capacity |
Use Case: Web servers, cloud microservices.
| Feature | Monolithic | Microservices |
|---|---|---|
| Codebase | Single large codebase | Multiple independent services |
| Deployment | Single deployment | Independent deployments |
| Scalability | Limited | High |
| Fault Isolation | Poor | Good |
| Example | Legacy ERP | Amazon, Netflix |
Answer:
Docker enables containerization, packaging apps with dependencies to run consistently across environments.
Benefits:
Portability
Isolation
Resource efficiency
Commands:
docker build → Build image
docker run → Run container
docker ps → List containers
Answer:
Kubernetes orchestrates containerized applications across clusters.
Key Concepts:
Pod: Smallest deployable unit
Node: Worker machine
Cluster: Master + multiple nodes
Service: Networking abstraction
Use Case: Auto-scaling microservices in production.
| Feature | CI/CD | DevOps |
|---|---|---|
| Focus | Automation of builds/tests/deployment | Culture + processes + tools |
| Scope | Part of DevOps pipeline | End-to-end software lifecycle |
| Example | Jenkins pipeline | CI/CD + Monitoring + Infrastructure as Code |
Answer:
Caching improves system performance by storing frequently accessed data.
Strategies:
Write-through: Update cache + DB simultaneously
Write-back: Update cache only, write to DB later
Read-through: Cache auto-fetches from DB if missing
Cache eviction: LRU, LFU
Tools: Redis, Memcached
Sharding: Splits DB into smaller shards for scalability
Replication: Copies of DB for redundancy and fault tolerance
Use Case: High-traffic apps like Twitter, eBay
| Concept | Use | Example |
|---|---|---|
| ACID | SQL DB transactions | MySQL, Oracle |
| BASE | Eventual consistency | MongoDB, Cassandra |
| CAP | Distributed systems trade-offs | Cassandra (AP), HBase (CP) |
Used to distribute load among nodes
Minimizes rehashing when nodes are added/removed
Example: Distributed cache (Redis Cluster)
Reduces downtime during scaling
