Object-Oriented Programming (OOP) is a programming paradigm based on the concept of objects, which can contain data in the form of fields (attributes) and code in the form of methods (functions). The main principles of OOP include Encapsulation, Abstraction, Inheritance, and Polymorphism. Encapsulation hides the internal state of an object and only exposes relevant data through methods.

Abstraction simplifies complex systems by focusing on essential details. Inheritance allows a class to inherit properties and behaviors from another class, while Polymorphism allows different objects to respond to the same method in unique ways. OOP is widely used for creating modular and reusable code, making it easier to manage and maintain large software systems.

An Array is a collection of elements stored in contiguous memory locations, which allows constant-time access to elements by their index. However, resizing an array is expensive because it requires creating a new array and copying elements. A Linked List, on the other hand, is a collection of nodes where each node contains data and a reference to the next node.

Unlike arrays, linked lists allow efficient insertion and deletion of elements, especially at the beginning or in the middle, because no shifting of elements is required. However, they have slower access times for random access, as it requires traversal from the head node. Arrays are generally more memory-efficient, while linked lists are more flexible for dynamic operations.

Big O notation is a mathematical representation used to describe the efficiency of an algorithm, particularly its time or space complexity, in relation to the input size. It provides an upper bound on the growth rate of an algorithm’s resource usage as the input grows. For example, O(1) indicates constant time complexity, meaning the algorithm's performance is unaffected by input size.

O(n) indicates linear time complexity, where performance scales linearly with input size, and O(n^2) indicates quadratic time complexity, where performance grows quadratically with the input size. Big O notation helps compare algorithms and determine the most efficient one in terms of scalability, especially for large datasets.

A Hash Map is a data structure that stores key-value pairs and allows for efficient lookup, insertion, and deletion of elements. It uses a hash function to compute an index (hash code) into an array, from which the corresponding value can be retrieved or modified.

The main advantage of a Hash Map is that it provides average constant-time complexity, O(1), for operations such as search, insert, and delete, assuming a good hash function and a low number of collisions. Collisions occur when two keys hash to the same index, and they can be handled through techniques like chaining (using linked lists) or open addressing. Hash Maps are widely used in scenarios where fast access to data is required.

A Stack is a Last-In, First-Out (LIFO) data structure, where the most recently added element is the first to be removed. It follows a push and pop operation, where elements are pushed onto the top of the stack and popped from the top.

It is used in scenarios like function calls, undo operations, and expression evaluation. A Queue, on the other hand, is a First-In, First-Out (FIFO) data structure, where the first element added is the first to be removed. Elements are enqueued at the rear and dequeued from the front. Queues are used in scenarios like job scheduling, breadth-first search (BFS) algorithms, and managing tasks in multi-threaded systems.

Inheritance is one of the core principles of Object-Oriented Programming (OOP), allowing a class (child class or subclass) to inherit properties and behaviors (methods) from another class (parent class or superclass). The child class can extend or override the functionality of the parent class, enabling code reuse and reducing redundancy. For example, a Dog class might inherit from a general Animal class, gaining common attributes and methods like eat() or sleep(), while also adding its specific behaviors like bark().

Inheritance promotes hierarchical relationships between classes and allows for more manageable and maintainable code by enabling subclasses to specialize and extend the functionality of base classes.

A Binary Search Tree (BST) is a data structure in which each node has at most two children, referred to as the left and right children. For every node, the left child’s value is less than the node’s value, and the right child’s value is greater than the node’s value.

This property makes BSTs ideal for searching and sorting, as operations like search, insertion, and deletion can be done efficiently in O(log n) time on average, where n is the number of nodes. However, the performance depends on the tree’s balance; in the worst case (a degenerate tree), these operations can take O(n) time.

• Encapsulation: Bundling the data and methods that operate on the data into a single unit (class). It also restricts access to some of the object's components, protecting the integrity of the data by preventing external modification (using access modifiers like private, public, protected).
• Abstraction: Hiding the complex implementation details and showing only the essential features of the object. This helps to simplify interactions with the object and allows users to focus on high-level functionality.
• Inheritance: Allowing a new class to inherit properties and methods from an existing class. This promotes code reuse and creates a natural hierarchy.

Binary Search is a divide-and-conquer algorithm used to find an element in a sorted list or array. The basic idea is to repeatedly divide the search space in half. Initially, the search begins with the entire array.

The middle element is compared with the target value. If the target is less than the middle element, the search continues in the left half of the array, otherwise, it continues in the right half. This process is repeated until the target element is found or the search space becomes empty. Binary Search has a time complexity of O(log n), making it much faster than linear search (O(n)) for large datasets.

A deadlock in multi-threading occurs when two or more threads are blocked forever, waiting for each other to release resources. This happens when each thread holds a resource and waits for another resource that is locked by another thread. Deadlock typically arises in scenarios where there is a circular dependency between threads.

For example, thread A holds lock 1 and waits for lock 2, while thread B holds lock 2 and waits for lock 1. To prevent deadlock, techniques like acquiring locks in a specific order, using timeouts, or implementing a resource hierarchy are used. Deadlock detection algorithms can also be implemented to resolve such situations.

In Object-Oriented Programming (OOP), a Class is a blueprint or template for creating objects, defining the attributes (fields) and methods (functions) that the objects of that class will have.

A Class can be thought of as a type or a template that describes how objects of that type should behave.

An Object, on the other hand, is an instance of a class. When you create an object, you are allocating memory for it and initializing it with specific values. Each object can have different data (state), but it shares the methods defined by the class. In essence, the class defines the structure, and the object is the actual entity created from that structure.

A Constructor is a special method in Object-Oriented Programming (OOP) that is automatically called when an object of a class is instantiated.

The constructor's main purpose is to initialize the object's state (attributes) and perform any setup or resource allocation necessary for the object. Constructors can take parameters, allowing users to provide initial values when creating an object.

A Stack is a data structure that follows the Last In, First Out (LIFO) principle. This means that the last element inserted is the first one to be removed. The primary operations are push (add an element) and pop (remove an element). Stacks are used in scenarios like recursive function calls or undo operations.

A Queue, on the other hand, follows the First In, First Out (FIFO) principle, where the first element inserted is the first to be removed. The main operations are enqueue (add an element) and dequeue (remove an element). Queues are used in scenarios like scheduling tasks, breadth-first search, or managing requests in a server.

A Deadlock occurs in a multi-threaded program when two or more threads are blocked forever, each waiting for the other to release a resource. This situation typically arises when four conditions hold simultaneously:

• Mutual Exclusion: Only one thread can hold a resource at a time.
• Hold and Wait: A thread holds a resource and waits for others.
• No Preemption: Resources cannot be forcibly taken from a thread.
• Circular Wait: Threads form a cycle, each waiting for the next.

A Queue is a data structure that follows the First In, First Out (FIFO) principle. In a queue, elements are added at the rear (enqueue) and removed from the front (dequeue).

This makes it ideal for situations where the order of processing matters. Key operations include enqueue (adding an element) and dequeue (removing an element).Queues are used in various applications, such as scheduling tasks in operating systems, managing requests in web servers, and breadth-first search in graph algorithms. They are also used in scenarios where resources are allocated in the order they arrive, such as printer job queues or process scheduling.

 “Customer Obsession” resonates most with me. I firmly believe that the foundation of great software is built around understanding and meeting the needs of users. Whether it's creating seamless user interfaces, improving performance, or adding features that enhance the user experience, prioritizing the customer is key to success.

I’ve always focused on gathering user feedback and iterating on it to ensure that products evolve in alignment with customer demands. I admire Amazon’s dedication to providing exceptional customer service, and I want to contribute to that mission by building technology that consistently exceeds user expectations.

 In one of my past projects, I faced an issue where the application would crash intermittently under high load. To debug, I first reproduced the issue in a staging environment by simulating heavy traffic.

Using logging, I identified the part of the code that caused the crash, but the exact reason was unclear. I then profiled the application to look for memory leaks and race conditions. Finally, after analyzing the code and stack trace, I discovered that a concurrency issue led to deadlocks. I fixed it by implementing proper synchronization and stress-tested the app to ensure the issue was resolved. The fix improved system stability significantly.

 In a previous team project, one team member consistently missed deadlines, affecting the overall progress. I decided to approach the issue by having a one-on-one conversation to understand the root cause. It turned out they were struggling with time management and didn’t feel comfortable asking for help.

I offered support and suggested setting smaller, achievable goals. We also agreed on regular check-ins to track progress. By creating a supportive environment and fostering open communication, I helped them improve, and the team’s overall performance was enhanced. This experience taught me the value of empathy and proactive problem-solving in team dynamics.

To design a URL shortening service, I would focus on scalability, high availability, and efficiency. First, I’d generate a short URL by creating a unique hash of the original URL. A base62 encoding (using characters A-Z, a-z, and 0-9) would be used to ensure a compact short URL. The system would consist of a relational or NoSQL database (e.g., DynamoDB) to store mappings between the original URL and the short URL. I’d implement a distributed system to handle high traffic.

To handle scalability, I would use a microservices architecture with load balancing and caching mechanisms (e.g., Redis) to ensure fast lookups. Lastly, I’d ensure the system supports redirects and analytics for tracking usage.

 Amazon has 16 Leadership Principles, such as "Customer Obsession," "Bias for Action," and "Invent and Simplify." I would apply these by always focusing on the customer and striving for innovation in my work. For instance, when faced with a problem, I would quickly analyze the root cause, prioritize a solution that adds the most value for the customer, and take calculated risks to innovate.

By embracing "Dive Deep," I would dive into technical details and ensure that solutions are built with efficiency and quality. “Deliver Results” would keep me focused on delivering value within time constraints while continuously improving my processes for better outcomes.

• final: Used to define constants, prevent method overriding, and prevent inheritance. For example, a final method cannot be overridden, and a final variable cannot be re-assigned after initialization.
• finally: A block used in exception handling. It executes regardless of whether an exception occurs or not, ensuring that resources are released or certain actions are performed, like closing a file stream.

• finalize: A method called by the garbage collector before an object is destroyed. It can be overridden to perform cleanup actions like releasing resources before the object is reclaimed.

I take a structured approach to learning new technologies. First, I identify the core concepts by reading documentation, books, and watching tutorial videos. I then build small projects to apply the concepts hands-on and deepen my understanding.

I often refer to open-source projects to see how experienced developers approach problems. Additionally, I participate in forums like Stack Overflow or join developer communities to exchange knowledge. I also set clear goals, like mastering a framework or completing a course within a set period, to stay motivated and track my progress. This approach helps me quickly get up to speed with new technologies.

 In such situations, I believe in approaching the disagreement with respect and an open mind. First, I would seek to understand their perspective by asking clarifying questions. If I still believe my approach is more effective, I would present my case with data and evidence, outlining the benefits of my perspective.

I’d try to focus on the issue rather than personal opinions. If we still disagree, I’d be open to compromising, understanding that decisions are sometimes made based on factors beyond technical merits. Ultimately, my goal is to collaborate effectively and contribute to the success of the team and the project.

 Debugging multi-threaded applications requires careful attention to race conditions, deadlocks, and synchronization issues. I begin by reproducing the issue consistently and analyzing the logs to identify the point of failure. Using tools like thread dumps, I look for thread activity, including which threads are stuck or waiting.

I then inspect synchronization mechanisms such as synchronized blocks, locks, and semaphores to ensure they’re used correctly. If necessary, I introduce logging to track the sequence of events across threads. I also consider using specialized debugging tools like Java’s VisualVM or Eclipse Debugger to inspect thread behavior and state in real-time.

• Singleton: Used to ensure that only one instance of a class exists. I used this to manage configuration settings in an application.Observer: Used for building event-driven systems. I used this pattern in a notification service where multiple components needed to listen to events like user actions.
• Factory: Helps in creating objects without specifying the exact class. I used this in a game engine to generate different types of enemies dynamically.
• Strategy: Used to define a family of algorithms and make them interchangeable. I used it to provide various payment methods in an e-commerce application, allowing easy addition of new payment method.