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1. Programming Languages

JavaScript (Node.js), Python, Java, Ruby, Go, or PHP: Pick one language and get comfortable with syntax & basics.


2. Version Control

Git: Learn version control basics, commit changes, branching, and collaboration on GitHub/GitLab.


3. Databases

Relational Databases: Master SQL basics with databases like MySQL or PostgreSQL. Learn how to design schemas, write efficient queries, and perform joins.
NoSQL Databases: Understand when to use NoSQL (MongoDB, Cassandra) vs. SQL. Learn data modeling for NoSQL.


4. APIs & Web Services

REST APIs: Learn how to create, test, and document RESTful services using tools like Postman.
GraphQL: Gain an understanding of querying and mutation, and when GraphQL may be preferred over REST.
gRPC: Explore gRPC for high-performance communication between services if your stack supports it.


5. Server & Application Frameworks

Frameworks: Master backend frameworks in your chosen language (e.g., Express for Node.js, Django for Python, Spring Boot for Java).
Routing & Middleware: Learn how to structure routes, manage requests, and use middleware.


6. Authentication & Authorization

JWT: Learn how to manage user sessions and secure APIs using JSON Web Tokens.
OAuth2: Understand OAuth2 for third-party authentication (e.g., Google, Facebook).
Session Management: Learn to implement secure session handling and token expiration.


7. Caching

Redis or Memcached: Learn caching to optimize performance, improve response times, and reduce load on databases.
Browser Caching: Set up HTTP caching headers for browser caching of static resources.


8. Message Queues & Event-Driven Architecture

Message Brokers: Learn message queues like RabbitMQ, Kafka, or AWS SQS for handling asynchronous processes.
Pub/Sub Pattern: Understand publish/subscribe patterns for decoupling services.


9. Microservices & Distributed Systems

Microservices Design: Understand service decomposition, inter-service communication, and Bounded Contexts.
Distributed Systems: Learn fundamentals like the CAP Theorem, data consistency models, and resiliency patterns (Circuit Breaker, Bulkheads).


10. Testing & Debugging

Unit Testing: Master unit testing for individual functions.
Integration Testing: Test interactions between different parts of the system.
End-to-End (E2E) Testing: Simulate real user scenarios to verify application behavior.
Debugging: Use logs, debuggers, and tracing to locate and fix issues.

11. Containerization & Orchestration

Docker: Learn how to containerize applications for easy deployment and scaling.
Kubernetes: Understand basics of container orchestration, scaling, and management.


12. CI/CD (Continuous Integration & Continuous Deployment)

CI/CD Tools: Familiarize yourself with tools like Jenkins, GitHub Actions, or GitLab CI/CD.
Automated Testing & Deployment: Automate tests, builds, and deployments for rapid development cycles.


13. Cloud Platforms

AWS, Azure, or Google Cloud: Learn basic cloud services such as EC2 (compute), S3 (storage), and RDS (databases).
Serverless Functions: Explore serverless options like AWS Lambda for on-demand compute resources.


14. Logging & Monitoring

Centralized Logging: Use tools like ELK Stack (Elasticsearch, Logstash, Kibana) for aggregating and analyzing logs.
Monitoring & Alerting: Implement real-time monitoring with Prometheus, Grafana, or CloudWatch.


15. Security

Data Encryption: Encrypt data at rest and in transit using SSL/TLS and other encryption standards.
Secure Coding: Protect against common vulnerabilities (SQL injection, XSS, CSRF).
Zero Trust Architecture: Learn to design systems with the principle of least privilege and regular authentication.


16. Scalability & Optimization

Load Balancing: Distribute traffic evenly across servers.
Database Optimization: Learn indexing, sharding, and partitioning.
Horizontal vs. Vertical Scaling: Know when to scale by adding resources to existing servers or by adding more servers.

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NoSQL vs SQL

NoSQL databases provide flexible data models ideal for diverse data structures and scalability.

1. Key-Value: Simple, uses key-value pairs (e.g., Redis).
2. Document: Stores data in JSON/BSON documents (e.g., MongoDB).
3. Graph: Manages complex relationships with nodes and edges (e.g., Neo4j).
4. Column Store: Optimized for analytics, organizes data by columns (e.g., Cassandra).

SQL databases, like RDBMS and OLAP, provide structured, relational storage for traditional and analytical needs

1. RDBMS: Traditional relational databases with tables (e.g., PostgreSQL & MySQL).
2. OLAP: Designed for complex analysis and multidimensional data (e.g., SQL Server Analysis Services).

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DSA (Data Structures and Algorithms) Essential Topics for Interviews

1️⃣ Arrays and Strings

Basic operations (insert, delete, update)

Two-pointer technique

Sliding window

Prefix sum

Kadane’s algorithm

Subarray problems


2️⃣ Linked List

Singly & Doubly Linked List

Reverse a linked list

Detect loop (Floyd’s Cycle)

Merge two sorted lists

Intersection of linked lists


3️⃣ Stack & Queue

Stack using array or linked list

Queue and Circular Queue

Monotonic Stack/Queue

LRU Cache (LinkedHashMap/Deque)

Infix to Postfix conversion


4️⃣ Hashing

HashMap, HashSet

Frequency counting

Two Sum problem

Group Anagrams

Longest Consecutive Sequence


5️⃣ Recursion & Backtracking

Base cases and recursive calls

Subsets, permutations

N-Queens problem

Sudoku solver

Word search


6️⃣ Trees & Binary Trees

Traversals (Inorder, Preorder, Postorder)

Height and Diameter

Balanced Binary Tree

Lowest Common Ancestor (LCA)

Serialize & Deserialize Tree


7️⃣ Binary Search Trees (BST)

Search, Insert, Delete

Validate BST

Kth smallest/largest element

Convert BST to DLL


8️⃣ Heaps & Priority Queues

Min Heap / Max Heap

Heapify

Top K elements

Merge K sorted lists

Median in a stream


9️⃣ Graphs

Representations (adjacency list/matrix)

DFS, BFS

Cycle detection (directed & undirected)

Topological Sort

Dijkstra’s & Bellman-Ford algorithm

Union-Find (Disjoint Set)


10️⃣ Dynamic Programming (DP)

0/1 Knapsack

Longest Common Subsequence

Matrix Chain Multiplication

DP on subsequences

Memoization vs Tabulation


11️⃣ Greedy Algorithms

Activity selection

Huffman coding

Fractional knapsack

Job scheduling


12️⃣ Tries

Insert and search a word

Word search

Auto-complete feature


13️⃣ Bit Manipulation

XOR, AND, OR basics

Check if power of 2

Single Number problem

Count set bits

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Topic: Data Structures – Trees – Top 15 Interview Questions with Answers

---

### 1. What is a tree data structure?

A hierarchical structure with nodes connected by edges, having a root node and child nodes with no cycles.

---

### 2. What is the difference between binary tree and binary search tree (BST)?

A binary tree allows up to two children per node; BST maintains order where left child < node < right child.

---

### 3. What are the types of binary trees?

Full, perfect, complete, skewed (left/right), and balanced binary trees.

---

### 4. Explain tree traversal methods.

Inorder (LNR), Preorder (NLR), Postorder (LRN), and Level Order (BFS).

---

### 5. What is a balanced tree? Why is it important?

A tree where the height difference between left and right subtrees is minimal to ensure O(log n) operations.

---

### 6. What is an AVL tree?

A self-balancing BST maintaining balance factor (-1, 0, 1) with rotations to balance after insert/delete.

---

### 7. What are rotations in AVL trees?

Operations (Left, Right, Left-Right, Right-Left) used to rebalance the tree after insertion or deletion.

---

### 8. What is a Red-Black Tree?

A balanced BST with red/black nodes ensuring balance via color rules, offering O(log n) operations.

---

### 9. How does a Trie work?

A tree structure used for storing strings, where nodes represent characters, allowing fast prefix searches.

---

### 10. What is the height of a binary tree?

The number of edges on the longest path from root to a leaf node.

---

### 11. How do you find the lowest common ancestor (LCA) of two nodes?

By traversing from root, checking if nodes lie in different subtrees, or by storing parent pointers.

---

### 12. What is the difference between DFS and BFS on trees?

DFS explores as far as possible along branches; BFS explores neighbors level by level.

---

### 13. How do you detect if a binary tree is a BST?

Check if inorder traversal yields a sorted sequence or verify node values within valid ranges recursively.

---

### 14. What are leaf nodes?

Nodes with no children.

---

### 15. How do you calculate the number of nodes in a complete binary tree?

Using the formula: number\_of\_nodes = 2^(height + 1) - 1 (if perfect), else traverse and count.

---

### Exercise

Write functions for inorder, preorder, postorder traversals, check if tree is BST, and find LCA of two nodes.

---

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