SDE Preparation Guide

Brief understanding of topic for interview

We will be focusing on the important topic for interview

Data Structure And Algorithms

Arrays — Ideal for storing and accessing elements with constant time indexing. Application: Efficient for tasks requiring ordered data or frequent random access.

Linked Lists — Effective for dynamic data storage and manipulation. Application: Often used in memory management, task scheduling, and implementing stacks/queues.

Stacks — Great for managing function calls, expression evaluation, and undo operations. Application: Balancing parentheses, converting infix to postfix notation.

Queues — Valuable for managing tasks in a first-in-first-out manner. Application: BFS in graph traversal, print spooling, scheduling.

Hash Tables — Ideal for quick data retrieval using key-value pairs. Application: Dictionaries, caching, frequency counting.

Trees — Crucial for hierarchical data representation and searching. Application: Binary Search Trees (BST) for efficient searching, AVL trees for balanced storage.

Heaps — Perfect for finding maximum/minimum elements efficiently. Application: Priority Queues, Dijkstra’s algorithm.

Graphs — Essential for modeling relationships between elements. Application: Social networks, shortest path algorithms, network flow.

Hash Maps — Valuable for associating keys with values for quick lookups. Application: Implementing symbol tables, databases, caching.

Trie — Useful for efficient string search and autocomplete features. Application: Dictionary implementation, spell checkers.

Disjoint-Set — Ideal for tracking partitioned sets with union and find operations. Application: Detecting connected components, Kruskal’s minimum spanning tree algorithm.

Bloom Filters — Efficient for membership testing in large datasets. Application: Spell checkers, network routers, duplicate detection.

Operation System

Key Components:
Process Management: Scheduling and execution of processes to ensure optimal CPU utilization.
Memory Management: Allocation and protection of memory for processes, including virtual memory techniques.
File System: Organization, storage, and retrieval of files with features like I/O and permissions.
Device Management: Interaction with hardware devices through drivers and I/O operations.
User Interface: Command-line or graphical interfaces for user interaction.
Kernel: Core component providing essential services and system call interfaces.

2. Types of OS:
Single-tasking OS: Handles one task at a time (e.g., early versions of MS-DOS).
Multi-tasking OS: Manages multiple tasks concurrently (e.g., Windows, Linux).
Real-time OS: Guarantees timely execution of tasks (e.g., embedded systems, robotics).
Distributed OS: Coordinates tasks across multiple interconnected computers (e.g., clusters).

3. Process vs. Thread:
Process: An independent program in execution, with its own memory space.
Thread: Smaller unit within a process, shares memory but has its own execution context.

4. Memory Management Techniques:
Paging: Divides physical memory into fixed-size blocks; virtual memory pages map to physical frames.
Segmentation: Divides memory into logical segments for better organization.

5. File System Concepts:
Files: Collections of data organized in a hierarchical structure.
Directories: Containers for files and subdirectories.
File Operations: Create, read, write, delete, and manipulate files.
File Permissions: Control access rights to files and directories.

6. Synchronization and Deadlocks:
Synchronization: Coordinating access to shared resources among multiple processes/threads.
Deadlock: A situation where processes are stuck, each waiting for a resource held by another.

7. Networking and Security:
Networking: OS facilitates communication between computers over networks.
Security: Enforces user authentication, authorization, and data protection.

8. Boot Process:
Bootstrapping: OS initialization process during system startup.

9. Examples of OS:
Windows (Microsoft)
macOS (Apple)
Linux (Various distributions like Ubuntu, Fedora)
Unix (Various variants)

Computer Networks

Key Components:
Nodes: Devices connected in a network (computers, printers, switches).
Links: Physical or logical connections (wired or wireless).
Router: For directing data between different networks.
Gateway: Connects different networks using different protocols.
Switch: Connects devices within the same network, forwarding data based on MAC addresses.

2. Network Topologies:
Star: All devices connected to a central hub.
Bus: Devices connected linearly along a single cable.
Ring: Devices connected in a circular arrangement.
Mesh: Every device connects to every other device.

3.Network Layers:
Application Layer: User-level services like HTTP, SMTP.
Transport Layer: Ensures data delivery and manages flow (TCP, UDP).
Network Layer: Routing and logical addressing (IP, ICMP).
Data Link Layer: Frames and MAC addresses (Ethernet, Wi-Fi).
Physical Layer: Actual data transmission over the medium.

Protocols:
TCP/IP: Suite of protocols for internet communication.
HTTP/HTTPS: Web browsing.
SMTP/POP3/IMAP: Email services.
FTP/SFTP: File transfer protocols.
DNS: Resolves domain names to IP addresses.
ARP: Maps IP addresses to MAC addresses.

IP Addressing:
IPv4: 32-bit address in four decimal-separated segments.
IPv6: 128-bit address in eight groups of hexadecimal segments.

Subnetting:
Dividing an IP network into smaller sub-networks (subnets) for efficient use of addresses.

Routing:
Determining the best path for data to travel from source to destination.

Firewalls and Security:
Firewalls control network traffic and protect against unauthorized access.
VPNs (Virtual Private Networks) create secure connections over public networks.

Network Address Translation (NAT):
Translates private IP addresses to a single public IP address for internet access.

Wireless Networks:
Wi-Fi (802.11) standards for wireless communication.
SSID, WPA, WPA2 for network security.

OSI Model vs. TCP/IP Model:
OSI: 7-layer model; TCP/IP: 4-layer model.

System Design

Requirements Analysis:
Understand the problem domain, user needs, and functional requirements.
Define use cases and scenarios to guide system design.

Scalability:
Design to handle increasing loads by adding resources or nodes.
Horizontal scaling (adding more machines) vs. vertical scaling (increasing machine power).

Availability and Reliability:
Redundancy and failover mechanisms to ensure uninterrupted service.
Use load balancers, replication, and backup strategies.

Database Design:
Choose appropriate database systems (SQL, NoSQL) based on data characteristics.
Optimize database schema for efficient data retrieval and storage.

Caching:
Implement caching mechanisms (in-memory caches like Redis) to reduce database load and improve performance.

Microservices vs. Monoliths:
Decide between a single large application (monolith) or modular, independent services (microservices).

API Design:
Design clean, well-documented APIs for communication between system components.
Use RESTful principles or GraphQL as needed.

Message Queues and Event-Driven Architecture:
Use message queues (e.g., Kafka, RabbitMQ) for decoupling and handling asynchronous tasks.

Security:
Implement authentication and authorization mechanisms.
Encrypt data at rest and in transit, and follow best practices for securing APIs.

Load Balancing:
Distribute incoming network traffic across multiple servers to ensure even workload distribution.

Data Partitioning and Sharding:
Split data into smaller subsets (shards) to improve performance and manage large datasets.

Latency and Performance:
Optimize algorithms and use appropriate data structures.
Use content delivery networks (CDNs) for faster content delivery.

Design Patterns:
Apply common design patterns (Singleton, Factory, Observer, etc.) for reusable and maintainable code.

Testing and Monitoring:
Design automated testing strategies and implement monitoring tools for performance analysis.

Version Control and Deployment:
Use version control systems (Git) and automated deployment pipelines (CI/CD).

Documentation:
Create clear, comprehensive documentation for code, architecture, and APIs.

Trade-offs:
Be prepared to discuss and justify design decisions, considering trade-offs between performance, complexity, and maintainability.

DBMS

Data Modeling:
Choose appropriate data models (relational, document, graph) based on data requirements.
Define entities, attributes, relationships, and constraints.

Relational Databases:
Use tables to store data in rows and columns.
Define primary keys for unique identification and establish relationships with foreign keys.

Normalization:
Organize data to minimize redundancy and ensure data integrity.
Follow normal forms (1NF, 2NF, 3NF) to eliminate data anomalies.

Indexes:
Improve query performance by creating indexes on frequently queried columns.
Understand trade-offs between indexing and insert/update operations.

SQL (Structured Query Language):
Use SQL for data manipulation (SELECT, INSERT, UPDATE, DELETE) and data definition (CREATE, ALTER, DROP).
Write efficient queries and understand query optimization.

Transactions and ACID Properties:
Ensure data consistency and integrity with Atomicity, Consistency, Isolation, and Durability properties.
Use transactions for grouped operations that must succeed or fail together.

Joins:
Combine data from multiple tables using various types of joins (INNER, OUTER, LEFT, RIGHT).
Optimize join operations for performance.

Views and Materialized Views:
Create virtual tables (views) or precomputed tables (materialized views) for simplified querying.

NoSQL Databases:
Choose appropriate NoSQL databases (key-value, document, column-family, graph) for specific use cases.
Understand eventual consistency and trade-offs.

Partitioning and Sharding:
Distribute data across multiple machines (partitioning) or databases (sharding) for scalability.

Replication:
Duplicate data across multiple servers for improved availability and fault tolerance.

Normalization vs. Denormalization:
Decide when to normalize (minimize redundancy) or denormalize (improve read performance) data.

Backup and Recovery:
Implement regular data backups and define recovery strategies in case of data loss.

Concurrency Control:
Manage simultaneous data access by multiple users using locking or isolation levels.

Data Warehousing:
Design and manage data warehouses for analytical processing and reporting.

ETL (Extract, Transform, Load):
Extract data from various sources, transform it, and load it into a data warehouse.

Database Design Patterns:
Apply patterns like Entity-Attribute-Value (EAV) or Time-Series for specific scenarios.

Normalization vs. Performance:
Balance normalization for data integrity and performance considerations.

We will be going through each topic and sub-topic one by one