SDE 3 and Above Interview Preparation Guide

A comprehensive guide for senior software engineering interviews, covering DS/Algo, System Design, and Leadership aspects.

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Rishabh Gupta
Engineering Leader | Generative AI Innovator | Python Expert | 0→1 Builder LinkedIn | GitHub | Twitter

📧 Contact: gupta.rishabh2912@gmail.com

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Table of Contents

1. Tackling DS/Algo Questions
2. Core Topics
   • Data Structures & Algorithms (DSA)
   • SOLID Principles & Design Patterns
   • YAGNI, DRY, and KISS Principles
   • Low-Level Design (LLD)
   • High-Level Design (HLD)
   • Behavioral & Leadership Interviews
   • Advanced Programming Concepts
   • Mock Interviews & Timed Practice
   • System Design Deep Dive
   • CI/CD & DevOps Knowledge
   • Cross-Functional Collaboration & Leadership

Tackling DS/Algo Questions

1. Understand the Problem Statement

   • Clarify Requirements: Carefully read the problem. Clarify ambiguities by asking questions to ensure a full understanding of inputs, outputs, edge cases, and constraints.
   • Restate the Problem: Summarize the problem to the interviewer in your own words to confirm your understanding.
   • Identify Constraints: Understand the time and space complexity constraints and ensure your solution will meet these requirements.

2. Approach & High-Level Plan

   • Brute Force Solution: First, explain a brute force approach, even if it's inefficient. This demonstrates that you know how to start tackling the problem.
   • Optimal Approach: After explaining brute force, move to an optimized approach. Discuss potential data structures or algorithms that could make the solution more efficient (e.g., binary search, dynamic programming, etc.).
   • Talk Out Loud: Share your thought process with the interviewer. Highlight your considerations like edge cases, time/space complexity trade-offs, and whether the problem can be solved iteratively or recursively.

3. Optimization and Complexity Analysis

   • Analyze Time & Space Complexity: Calculate the complexity of your approach using Big O notation.
   • Optimize: If there’s room for improvement, explain how you could optimize the solution further by using better algorithms or more efficient data structures.

4. Pseudocode

   • Draft Pseudocode: Once you've discussed and optimized your solution, outline the logic in pseudocode.
   • Structured Approach: Focus on structuring your logic with clarity. Break it into small, manageable functions or steps, ensuring modularity.
   • Edge Cases: Mention edge cases you’ll handle in your pseudocode (e.g., empty inputs, duplicates, large datasets, etc.).

5. Implementation

   • Write Clean Code: Implement the solution in Python with readable and maintainable code. Follow Pythonic principles, and make sure you write clean, self-explanatory code.
     • Use Classes & Functions: If applicable, encapsulate your logic into classes or functions. Follow SOLID principles like single responsibility and open/closed principles.
     • Use Design Patterns: Apply design patterns where applicable. For instance, if you need to manage different algorithms, consider the Strategy pattern. If using recursion, ensure it’s clear and optimized (e.g., using memoization).
   • Code Modularity: Break down your solution into clear methods/functions. Each method should do one thing well.
   • Exception Handling: Add meaningful error and exception handling to ensure robustness.

6. Test Your Solution

   • Test Cases: Start with basic test cases and then handle edge cases. Mention how you would test for performance, such as handling large input sizes.
   • Dry Run: Walk through the code with an example to demonstrate its correctness and efficiency.

7. Reflection & Further Optimization

   • Discuss Limitations: Reflect on your solution, stating where it could be improved or scaled further.
   • Iterate if Needed: If time allows, consider further improvements or refactor sections to make them cleaner or more efficient.

By following this structured process, you’ll demonstrate problem-solving ability, code optimization skills, and a solid understanding of software engineering principles, which are crucial at the SDE 3 level.

Core Topics

Data Structures & Algorithms (DSA)

• Core Topics: Arrays, Strings, Hashing, Binary Search, Sorting, Two Pointers, Sliding Window.
• Advanced Topics: Recursion, Dynamic Programming, Graphs (BFS, DFS), Trees, Greedy Algorithms, Divide and Conquer, Backtracking, Bit Manipulation.
• Resources: Neetcode’s 150/Blind 75, Leetcode, Cracking the Coding Interview.

SOLID Principles & Design Patterns

• SOLID Principles: Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion.
• Design Patterns: Creational (Factory, Singleton), Structural (Adapter, Decorator), Behavioral (Observer, Strategy).
• Resources: Refactoring.Guru, “Head First Design Patterns,” Python examples.

YAGNI, DRY, and KISS Principles

• Key Concepts: YAGNI (You Aren’t Gonna Need It), DRY (Don’t Repeat Yourself), KISS (Keep It Simple, Stupid).
• Resources: “Clean Code” by Robert Martin, examples in daily coding practice.

Low-Level Design (LLD)

• Key Focus: Object-oriented design, Class diagrams, Design of systems like Parking Lot, ATM, Library Management.
• Resources: Grokking the Object-Oriented Design Interview, LLD practice problems.

High-Level Design (HLD)

• Key Focus: System design (load balancers, databases, caching, sharding, microservices, scaling).
• Topics to Study: CAP theorem, database replication, distributed systems, event-driven architecture.
• Resources: System Design Primer, “Designing Data-Intensive Applications” by Martin Kleppmann, HLD mock interviews.

Behavioral & Leadership Interviews

• Key Focus: Leadership, teamwork, conflict resolution, ownership.
• Method: STAR (Situation, Task, Action, Result) for structuring responses.
• Common Questions: Leadership in tight deadlines, conflict with team members, handling large projects.
• Resources: “Cracking the PM Interview,” Mock interviews with peers.

Advanced Programming Concepts

• Python Deep Dive: Asyncio, Decorators, Generators, Memory management, Threading.
• Testing: Unit testing, Integration testing, PyTest for Python.
• Resources: Fluent Python, Effective Python, Code review practice.

Mock Interviews & Timed Practice

• Key Focus: Simulate interview environments, solve problems under time constraints.
• Resources: Leetcode, Pramp, Interviewing.io, peer mock interviews.

System Design Deep Dive

• Key Topics: Tech stack selection, trade-offs in system design (performance vs. consistency, availability vs. partitioning).
• Real-World Examples: Systems like Netflix, Uber, Facebook, WhatsApp architecture.
• Resources: Read case studies, design large-scale systems, discuss with peers.

CI/CD & DevOps Knowledge

• Key Focus: Tools like Docker, Kubernetes, Jenkins, AWS architecture, scaling systems.
• Resources: AWS documentation, DevOps tools guides, Kubernetes workshops.

Cross-Functional Collaboration & Leadership

• Key Focus: Leading teams, cross-functional collaboration, conflict resolution.
• Resources: Leadership-focused mock interviews, case studies on team management.

📌 Fundamental Concepts

• Basics OOP Concepts
• SOLID Principles with Pictures
• SOLID Principles with Code
• DRY Principle
• YAGNI Principle
• KISS Principle
• Coursera - Object-Oriented Design

OOPs Concepts:

1. Abstraction
   • Hiding the complex implementation details and showing only the necessary parts to the user.
2. Encapsulation
   • Binding the data (attributes) and code (methods) that operates on the data into a single unit.
3. Inheritance
   • Inheritance is a mechanism where a class acquires the properties and behaviors of another class.
4. Polymorphism
   • Polymorphism allows you to define a single interface or method in a base class and have multiple derived classes implement or override that method.

SOLID Principles:

1. Single Responsibility Principle
   • A class should have only one responsibility.
2. Open/Closed Principle
   • Software entities (classes, modules, functions, etc.) should be open for extension but closed for modification.
3. Liskov Substitution Principle
   • Objects of a superclass should be replaceable with objects of a subclass without affecting the correctness of the program.
4. Interface Segregation Principle
   • Clients should not be forced to depend on interfaces they do not use.
5. Dependency Inversion Principle
   • High-level modules should not depend on low-level modules. Both should depend on abstractions.
   • Abstractions should not depend on details. Details should depend on abstractions.

Design Patterns:

https://refactoring.guru/design-patterns

Creational Patterns

Creational patterns deal with object creation mechanisms, trying to create objects in a manner suitable to the situation.

• Singleton: Ensures a class has only one instance and provides a global point of access to it.
• Factory: Defines an interface for creating objects, but lets subclasses alter the type of objects that will be created.
• Abstract Factory: Provides an interface for creating families of related or dependent objects without specifying their concrete classes.
• Builder: Separates the construction of a complex object from its representation, allowing the same construction process to create different representations.
• Prototype: Creates new objects by copying an existing object, known as the prototype.
• Object Pool: Manages a pool of reusable objects, optimizing the performance by reusing objects rather than creating and destroying them frequently.

Structural Patterns

Structural patterns deal with object composition or the structure of classes and objects, ensuring that if one part changes, the entire structure can still function properly.

• Adapter: Allows incompatible interfaces to work together, converting one interface into another that a client expects.
• Bridge: Separates an object’s abstraction from its implementation so that the two can vary independently.
• Composite: Composes objects into tree structures to represent part-whole hierarchies, allowing clients to treat individual objects and compositions of objects uniformly.
• Decorator: Adds additional responsibilities to an object dynamically, providing a flexible alternative to subclassing for extending functionality.
• Facade: Provides a simplified interface to a complex subsystem, making it easier to use.
• Flyweight: Reduces the cost of creating and manipulating a large number of similar objects by sharing as much data as possible.
• Proxy: Provides a surrogate or placeholder for another object to control access to it.

Behavioral Patterns

Behavioral patterns deal with communication between objects, outlining patterns of how objects interact and distribute responsibility.

• Chain of Responsibility: Passes a request along a chain of handlers, allowing multiple objects an opportunity to handle the request without coupling the sender with the receiver.
• Command: Encapsulates a request as an object, thereby allowing for parameterization of clients with queues, requests, and operations.
• Interpreter: Defines a grammar for a language and interprets sentences in the language.
• Iterator: Provides a way to access the elements of an aggregate object sequentially without exposing its underlying representation.
• Mediator: Defines an object that encapsulates how a set of objects interact, promoting loose coupling by keeping objects from referring to each other explicitly.
• Memento: Captures and restores an object's internal state without violating encapsulation.
• Observer: Defines a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically.
• State: Allows an object to alter its behavior when its internal state changes.
• Strategy: Defines a family of algorithms, encapsulates each one, and makes them interchangeable.
• Template Method: Defines the skeleton of an algorithm in a method, deferring some steps to subclasses.
• Visitor: Represents an operation to be performed on elements of an object structure, allowing one to define new operations without changing the classes of the elements on which it operates.

Common Questions:

DS & ALGO Common Patterns:

https://neetcode.io/roadmap video[https://www.youtube.com/watch?v=DjYZk8nrXVY]

1. Prefix Sum
2. Two Pointer
3. Sliding Window
4. Fast & Slow Pointer
5. Linked List In-Place Reversal
6. Monotonic Stack
7. Top 'k' Elements
8. Quick Select
9. Overlapping Intervals
10. Modified Binary Search
11. Depth-First Search(DFS)
12. Breadth-First Search(BFS)
13. Matrix Traversal
14. Backtracking
15. Dynamic Programming

LLD: - video[https://www.youtube.com/watch?v=OhCp6ppX6bg]

https://github.com/ashishps1/awesome-low-level-design Personal LLD Interview Questions

10 Common LLD Interview Problems

1. Design a parking lot Design a parking lot
2. Design a vending machine Design a vending machine
3. Design an elevator system Design an elevator system
4. Design LRU cache Design LRU cache
5. Design a chess game Design a chess game
6. Design snake and ladders Design snake and ladders
7. Design Splitwise Design Splitwise
8. Design logging framework Design logging framework
9. Design hotel management system Design hotel management system
10. Design movie ticket booking syetem Design movie ticket booking syetem

System Design: - video[https://www.youtube.com/watch?v=l3X1t3kpmwY]

https://github.com/ashishps1/awesome-system-design-resources

10 Common System Design Problems

1. Design a URL shortening service like TinyURL
2. Design a social media platform like Twitter/Instagram
3. Design a chat application like WhatsApp/Slack
4. Design a web crawler
5. Design a video streaming service like YouTube/Netflix
6. Design an e-commerce platform like Amazon
7. Design a ride-sharing service like Uber/Lyft
8. Design a notification system
9. Design a key-value store like Redis
10. Design a scalable logging and monitoring system

Behavioral & Leadership Interviews:

Common Questions

1. Leadership in Tight Deadlines
2. Conflict with Team Members
3. Handling Large Projects
4. Mentoring Junior Engineers
5. Workplace Conflict Resolution

Different Databases Comparisons:

ACID:

ACID is a set of four important properties that ensure reliable and consistent database transactions. Here's a simple breakdown:

1. Atomicity: A transaction must either be all completed or none at all. If one part fails, the entire transaction is rolled back, ensuring no partial changes.

   • Example: If you're transferring money between bank accounts, either the money is moved from account A to B entirely, or nothing happens.

2. Consistency: A transaction brings the database from one valid state to another. It must follow all rules (like unique IDs or foreign key constraints) to keep data correct.

   • Example: After transferring money, the total amount across all accounts remains the same.

3. Isolation: Transactions happening at the same time must not interfere with each other. Each transaction acts as if it's the only one running until it's finished.

   • Example: Two people withdrawing money at the same time won't mess up each other’s balances.

4. Durability: Once a transaction is committed, it is permanently saved. Even if there’s a power failure, the data will still be there after recovery.

   • Example: After confirming a money transfer, the change stays in the database even if the server crashes immediately after.

In short, ACID guarantees that database operations are reliable, accurate, and consistent, no matter what happens.

Key-Value 🐇: REDIS, MEMCACHE, AMAZON DYNAMODB, AEROSPIKE

• Flexible for Unstructured Data
• Fast Lookup
• In-Memory Database (except DynamoDB which has persistent storage)
• Not for Complex Data Structures
• Not for ACID transactions (DynamoDB provides eventual consistency)
• Not for Historical Data
• Ideal for Caching and Session Management

Wide-column 🎡: CASSANDRA, HBASE, SCYLLADB, BIGTABLE

• Column layout
• Primary Keys
• Denormalized
• Not for Random Filtering and Rich queries
• Not for Transaction Processing
• High scalability
• Optimized for Writes

Document 📑: MONGODB, COUCHBASE, COUCHDB, AMAZON DOCUMENTDB, FIRESTORE

• Denormalized
• Handle Unstructured Data
• Indexing and Rich Query
• Not for Complex joins and relationships
• Not for Referential integrity
• Most intuitive for JSON-based Data
• Flexible Schema Design
• Supports Eventual Consistency

Relational 👑: MYSQL, POSTGRESQL, ORACLE, SQL SERVER, AURORA, AMAZON RDS

• Mature and formalized datamodel
• Normalization
• Difficult to scale horizontally
• ACID transactions
• Rich Querying Capabilities
• Strong Data Integrity
• Well-suited for complex joins and referential integrity
• High Performance for OLTP and OLAP

Graph 🍇: NEO4J, AMAZON NEPTUNE, ORIENTDB, ARANGODB

• No need to compute the relationships at query time
• Handles Complex Data Structures
• Difficult to scale
• Not for Write-heavy workloads
• Multi-hop relationships
• Great for Social Networks, Fraud Detection, and Recommendation Engines
• Efficient for traversing complex relationships

Blob Storage 🌐: AWS S3, AZURE BLOB STORAGE, GOOGLE CLOUD STORAGE, MINIO

• Object Storage for Unstructured Data
• Durable and Scalable
• Eventual Consistency
• Not for Structured Data or Fast Lookups
• Suitable for Backup, Archive, and Media Content
• Supports Replication and Versioning
• Optimized for Large Files

Consistent Hashing

Consistent hashing is a technique used to distribute data across multiple servers (or nodes) in a way that reduces the impact of adding or removing servers. It's commonly used in distributed systems to evenly balance load without requiring massive redistributions when the system changes.

Key Idea:

In a traditional hash function, if you add or remove a server, you might have to reassign all keys (data) to different servers. Consistent hashing minimizes this reassignment, ensuring only a small portion of the keys are moved when servers are added or removed.

How it works (with an example):

1. The Hash Circle: Imagine a ring or circle where all possible hash values are laid out from 0 to the maximum hash value.

2. Servers as Points on the Circle: When you add servers, you hash their IP addresses (or identifiers) to get a position on this circle. For example, let’s say we have 3 servers, and their positions on the circle are at 10, 30, and 70.

3. Distributing Data (Keys) to Servers: Now, to place data (like files or records) onto the servers, we hash each key (data identifier) to a position on the same circle. A key is stored on the first server it "meets" while going clockwise around the circle.

   • Example: If a key hashes to 25, it goes to the server at position 30. If a key hashes to 65, it goes to the server at position 70.

4. Adding/Removing Servers:

   • Adding a Server: Say we add a new server at position 50. Now only keys between 30 and 50 will be moved to the new server. Other keys stay where they are, minimizing movement.
   • Removing a Server: If a server is removed (e.g., the one at position 70), only the keys that mapped to that server will need to be redistributed to the next available server (in this case, the server at position 10).

Example:

Let’s say we have 3 servers:

• Server A: Position 10
• Server B: Position 30
• Server C: Position 70

Now, we hash our data keys:

• Key X hashes to 25 → goes to Server B (position 30).
• Key Y hashes to 40 → goes to Server C (position 70).
• Key Z hashes to 75 → wraps around the circle and goes to Server A (position 10).

If we add a new server, Server D at position 50, only Key Y (which was between 30 and 70) moves to Server D, reducing the number of moved keys.

Benefits:

• Minimal key movement: Only a few keys are affected when servers are added or removed.
• Scalability: It works well for large, dynamic systems where servers frequently join or leave the network.

REMOTE JOBS: