SWE Technical Interview

Software Project Management

The application of knowledge, skills, and tools to manage software projects, including planning, resource allocation, scheduling, and risk management.

Programming Paradigms

The classification, style or way of programming. It is an approach to solve problems by using programming languages. Examples include imperative, procedural, functional, declarative, and object oriented.

Software Documentation

The creation of comprehensive and clear documentation, including user manuals, technical specifications, and design documents, to aid in software understanding and maintenance.

Main idea about TypeScript

The main idea behind TypeScript is to enhance JavaScript by adding static typing and additional features to help developers write more reliable and scalable code. TypeScript is a superset of JavaScript, meaning any valid JavaScript code is also valid TypeScript code. It introduces optional static typing and provides tools for type checking and compilation. Here are the key concepts and benefits of TypeScript: Static Typing: TypeScript allows developers to explicitly define types for variables, function parameters, and return values. By having a static type system, TypeScript can detect type errors during the development phase, catching potential bugs before the code is executed. This helps improve code quality, maintainability, and makes it easier to understand and navigate large codebases. Type Inference: TypeScript has a powerful type inference system that can automatically infer types based on the assigned values. This reduces the need for explicit type annotations and makes the language more expressive and concise. Enhanced Tooling: TypeScript provides advanced tooling support, including code editors with intelligent autocompletion, refactoring, and real-time error checking. It enables developers to catch errors and get helpful suggestions while writing code, leading to increased productivity and reduced debugging time. Modern JavaScript Features: TypeScript supports the latest ECMAScript (JavaScript) features and syntax, allowing developers to write code using the most up-to-date language features and compile it down to older versions of JavaScript that can run in all browsers. Improved Maintainability and Collaboration: With TypeScript's static typing, code becomes more self-documenting, making it easier to understand and maintain. It also enables better collaboration among team members as types provide clear contracts and expectations for how code should be used. Compatibility with JavaScript Ecosystem: TypeScript is designed to be highly compatible with existing JavaScript libraries and frameworks. It can seamlessly incorporate JavaScript code and libraries, allowing developers to gradually introduce TypeScript into their projects without requiring a complete rewrite. TypeScript is widely adopted and supported by major JavaScript frameworks like Angular and React, as well as popular development tools and editors. It provides developers with a safer and more efficient way to write JavaScript code, especially for larger-scale projects where maintainability and type safety are crucial.

Main idea about React

The main idea behind using React is to create dynamic and interactive user interfaces for web applications. React is a JavaScript library developed by Facebook that focuses on building reusable UI components. It employs a component-based architecture, which allows developers to break down the user interface into smaller, self-contained components. React uses a virtual DOM (Document Object Model) to efficiently update and render only the necessary parts of the user interface when there are changes, resulting in improved performance and responsiveness. It provides a declarative syntax, where developers describe what the UI should look like based on the application state, and React takes care of updating the actual DOM accordingly. One of the key benefits of React is its ability to efficiently handle large and complex applications by promoting code reusability and modularity. Components can be easily composed and reused throughout the application, making it easier to manage and maintain the codebase. React also promotes a unidirectional data flow, where data flows from parent components to child components, simplifying the state management and making it easier to reason about the application's behavior. React has gained significant popularity and a thriving ecosystem, with a wide range of supporting libraries and tools. It can be used to build single-page applications, mobile applications (using React Native), or even server-side rendering applications. Its popularity is largely attributed to its efficiency, flexibility, and community support, making it a preferred choice for many developers when building modern web applications.

Software Security

The practice of identifying, preventing, and mitigating security vulnerabilities and risks in software systems, including authentication, encryption, and secure coding practices.

Version Control

The practice of managing and tracking changes to source code, enabling collaboration, reverting to previous versions, and maintaining code integrity. Git is a popular version control system.

Software Development Life Cycle (SDLC)

The process of planning, designing, developing, testing, deploying, and maintaining software systems throughout their lifecycle. Examples include Waterfall, Agile, Iterative.

Principles of OOP

A programming paradigm that organizes code into objects, which are instances of classes. 1. Inheritance - In inheritance, there is a parent class and a child class. A child class inherits the properties and methods of the parent class. Reusability. We know sometimes that multiple places need to do the same thing, and they need to do everything the same except for one small part. This is a problem inheritance can solve. Whenever we use inheritance, we try to make it so that the parent and the child have high cohesion. Cohesion is how related your code is. 2. Polymorphism - You can have one function or object that can be used in different ways. Let's take an example of the addition operator (+); we can use it to add numbers, but we can also use it to concatenate strings. The real power of polymorphism is sharing behaviours, and allowing custom overrides. 3. Encapsulation - Encapsulation is the process of making data private by wrapping data and its methods in a 'capsule' or unit, so that it can not be accessed or modified outside of that unit. This is achieved by making variables inside a class private. Each object in your code should control its own state. Modularising and having clear responsibilities is key to Object Orientation. 4. Abstraction - To abstract something away means to hide away the implementation details inside something - sometimes a prototype, sometimes a function. So when you call the function you don't have to understand exactly what it is doing. Finding things that are similar in your code and providing a generic function or object to serve multiple places/with multiple concerns. (Like we do for PL, when we use repeated code that is just slightly different, create a helper that takes in the thing that is different. And makes it more readable.)

Algorithms and Data Structures

Algorithms are step-by-step procedures for solving problems, while data structures are the organization and storage formats for data in a program.

Interface (in programming, not ui)

An interface is used to declare a behavior that classes must implement. They are similar to protocols. Interfaces are declared using the interface keyword, and may only contain method signature and constant declarations. Interfaces are there so that our classes can specialize in what they do all the while implementing functionality that's common not only to that class but other classes that may need the same functionality.

Testing and Quality Assurance

The process of verifying and validating software to ensure that it meets functional requirements, performs as expected, and is free from defects.

Process for debugging

1. Reproduce the Issue: Start by understanding and reproducing the problem consistently. Identify the steps or conditions that lead to the issue. Reproducibility helps isolate the problem and ensures that any fixes applied can be verified. 2. Divide and Conquer: If the codebase is large or complex, narrow down the scope of the issue by using a "divide and conquer" approach. Temporarily remove or isolate sections of code to identify which part is causing the problem. This helps pinpoint the problematic code and reduces the search space. 3. Review Error Messages and Logs: Thoroughly examine error messages, warnings, and log files related to the issue. Understand the context, stack traces, and any additional information provided. Error messages can often provide clues about the source of the problem and the specific lines of code involved. 4. Use Debugging Tools: Debuggers: Utilize integrated development environment (IDE) debuggers or command-line debuggers. They allow you to step through code, set breakpoints, inspect variables, and observe program flow during runtime. Logging and Tracing: Add additional logging statements or enable tracing to output relevant information during execution. This helps track program flow, variable values, and identify areas of code where the issue might occur. 5. Code Inspection: Read the Code: Carefully read the code associated with the problematic area. Understand the logic, control flow, and variable usage to identify potential mistakes or overlooked details. Code Review: Seek assistance from colleagues or peers to review your code. A fresh set of eyes can often spot issues that might have been missed. 6. Test and Isolate Components: Develop test cases that specifically target the problematic code or feature. Isolate the component under test and verify its behavior in different scenarios. This approach helps narrow down the problem and ensure that any fixes applied do not introduce regressions. Check Input and Assumptions: Verify the input data and assumptions made by the code. Ensure that the code handles various edge cases, invalid inputs, or unexpected conditions correctly. Validate inputs and validate any assumptions made about the environment or dependencies. 7. Collaborate and Seek Help: Don't hesitate to seek assistance from colleagues, online communities, or dedicated support channels. Collaboration can bring fresh perspectives and ideas to help identify and resolve the problem more efficiently. Remember that effective debugging requires patience, attention to detail, and systematic investigation. It's essential to make incremental changes, test thoroughly, and document the debugging process to ensure a reliable and maintainable codebase.

React 101: 1. What causes components to re-render? 2. What's the difference between rendering and mounting?

1. State changes. setState and useState trigger a re-render. Prop changes. If a component receives new props from its parent component, the UI will update. Component mounting and unmounting causes a re-render. If a component uses React Context, component is re-rendered, meaning it reacts to changes in application-wide data. 2. Rendering: Rendering refers to the process of generating the output for a component or a tree of components. It involves creating a representation of the component's user interface elements (HTML, JSX, or other UI constructs) based on its current state and props. Rendering can occur multiple times throughout the lifecycle of a component, reflecting updates to the component's state or changes in the props it receives. Mounting: Mounting, on the other hand, specifically refers to the process of creating an instance of a component and inserting it into the DOM (Document Object Model) or the component hierarchy. It occurs when a component is first added to the DOM or when a component is re-added after being removed. To put it simply, rendering is the act of generating the UI output based on the current state and props of a component, while mounting is the process of adding that rendered output to the DOM. During the mounting process, React goes through several lifecycle methods (such as constructor, componentDidMount, etc.) that allow components to perform certain actions at specific stages of the mounting process, such as setting initial state, making API requests, or subscribing to event listeners. Once a component is mounted, it can undergo subsequent renderings triggered by state changes, prop updates, or other factors. However, mounting only occurs once during the component's lifecycle when it is first added to the DOM. Overall, rendering is the broader concept of creating the UI output, while mounting specifically refers to the process of adding that rendered output to the DOM.

Asynchronous programming

Asynchronous programming is a way of writing code that allows tasks to run independently and not block the execution of other tasks. It's like doing multiple things at the same time without having to wait for each one to finish before moving on to the next. Imagine you have a list of chores to do, such as cleaning, cooking, and doing laundry. In synchronous programming, you would do one chore at a time, starting with cleaning. You wouldn't move on to cooking until the cleaning is complete, and so on. This can be inefficient if one task takes a long time, as you're essentially waiting for it to finish before moving on to the next one. In asynchronous programming, you approach it differently. You start a task, let's say cleaning, and instead of waiting for it to finish, you move on to the next task, cooking, without blocking or pausing the program. The cleaning task continues running in the background, and once it's done, it notifies you or triggers a callback function. Meanwhile, you can start another task, like doing laundry, and so on. The key concept in asynchronous programming is that you don't wait for tasks to complete before moving on. Instead, you schedule tasks, let them run concurrently, and handle the results or notifications when they become available. This way, you can make more efficient use of your time and resources. Asynchronous programming is commonly used in situations where tasks involve waiting for external resources like network requests, file operations, or user input. By allowing the program to continue executing other tasks while waiting for these operations to complete, it helps avoid unnecessary delays and keeps the program responsive. In summary, asynchronous programming is like multitasking, where you start tasks and let them run concurrently, without waiting for each one to finish before moving on. It helps improve efficiency, responsiveness, and allows for better utilization of resources.

Parallelism

Asynchronous programming, as we discussed earlier, allows tasks to run independently without blocking the execution of other tasks. It enables you to initiate a task and then move on to another task without waiting for the first one to complete. This is achieved by utilizing techniques like callbacks, promises, or async/await in programming languages. The key idea behind asynchronous programming is to handle tasks that involve waiting for external operations (such as I/O operations or network requests) in a non-blocking manner. While a task is waiting for a response, the program can continue executing other tasks. Once the awaited result becomes available, the program is notified or triggers a callback to handle the result. On the other hand, parallelism focuses on simultaneously executing multiple tasks across multiple processors or computing resources. It involves breaking down a larger task into smaller subtasks and distributing those subtasks across different resources for simultaneous execution. Parallelism is all about achieving concurrency by dividing the workload and executing it in parallel. This can be done either by utilizing multiple cores in a single machine or by distributing the workload across multiple machines in a network. Parallelism is particularly useful when dealing with computationally intensive tasks that can be divided into independent parts. By executing these parts simultaneously, the overall execution time can be significantly reduced, leading to improved performance and efficiency. To summarize, asynchronous programming allows tasks to run independently without blocking the execution of other tasks, while parallelism focuses on simultaneously executing multiple tasks across multiple processors or computing resources. Asynchronous programming is concerned with non-blocking execution and responsiveness, while parallelism is about achieving concurrency and optimizing performance by distributing tasks across available resources.

Back-end engineering

Backend engineering refers to the development and maintenance of the server-side components and infrastructure that power websites, applications, and other software systems. It involves working on the "behind-the-scenes" aspects of a software project that users don't directly interact with. Think of a website as an iceberg: the part visible to users is the frontend, which includes the user interface and how it looks. The backend, however, is hidden beneath the surface and handles the data, logic, and interactions that make the frontend work. Backend engineers are responsible for building and maintaining the server-side logic, databases, APIs (Application Programming Interfaces), and other components that enable the functionality and communication of an application. They work with programming languages and frameworks specifically designed for server-side development, such as Python, Java, Ruby, or Node.js. In simplified terms, backend engineering is like building the engine of a car. It involves designing, building, and maintaining the complex systems that handle data, process requests, and ensure the smooth functioning of a software application. Backend engineers focus on things like handling data storage, managing user authentication, implementing business logic, integrating with external services, and optimizing performance and security. Backend engineering is crucial for creating scalable, secure, and robust software systems that can handle heavy traffic, store and retrieve data efficiently, and provide reliable services to users. It requires knowledge of programming languages, databases, server management, APIs, and various other tools and technologies. Processing user inputs: It takes the data submitted by users and performs necessary operations, such as validating the input, sanitizing it to prevent security issues, and transforming or formatting it as needed. Business rules and workflows: It implements the specific rules and logic that govern how the application functions. This may include calculations, decision-making processes, and handling different scenarios based on the application's requirements. Data storage and retrieval: Server-side logic manages the interaction with databases or external data sources. It handles queries, updates, and other operations to store and retrieve data needed by the application. Security and access control: It enforces security measures, including authentication and authorization, to ensure that users can access appropriate resources and that sensitive data is protected. Integration with external services: Server-side logic allows communication and integration with other systems, APIs, or third-party services. It enables exchanging data or triggering actions between the application and external entities. In summary, server-side logic is the computational and decision-making part of an application that operates on the server or backend. It processes user inputs, applies business rules, manages data storage, ensures security, and facilitates interactions with external services. It is responsible for making the application work and delivering the desired functionality to users.

Caching

Caching is like having a handy storage space that allows you to access things quickly and easily. Imagine you're working on a project and you need certain tools or materials frequently. Instead of going back and forth to get them every time, you set up a small table right next to you where you keep those items within arm's reach. This way, you can grab them instantly whenever you need them, without having to walk all the way to the storage room. In the context of computing, caching works in a similar way. When you use a computer or browse the internet, data is constantly being transferred between different parts of the system. Caching involves temporarily storing frequently accessed data in a faster and closer location to the processor or user, so that it can be retrieved quickly without having to fetch it from the original source every time. For example, web browsers use caching to store website data like images, scripts, and stylesheets on your computer. When you visit a website again, instead of downloading all the data from scratch, your browser checks the cache first. If the data is already stored in the cache and hasn't expired, it can be quickly retrieved, resulting in faster page loading times. Caching helps improve overall system performance by reducing the time and resources needed to access frequently used data. It's like having a handy storage space right next to you, making your work or browsing experience much faster and more efficient.

Steps to optimize code performance

Crucial for creating efficient and responsive software applications. 1. Algorithmic Optimization: Analyze algorithms: Evaluate algorithms and consider alternative algorithms that may provide better performance for specific use cases. Reduce complexity: Minimize time and space complexity. Identify and eliminate unnecessary loops, redundant calculations, or excessive memory usage. 2. Data Structures and Data Access: Choose appropriate data structures: Select best fitting data structures. Consider search, insertion, deletion, and access times for different data operations. 3. Code Optimizations: Use efficient loops: Reduce iterations, eliminate unnecessary operations within loops, and ensuring loop conditions are as efficient as possible. Avoid excessive function calls. Reduce memory allocations: Minimize unnecessary memory allocations by reusing objects. 4. Parallelization and Concurrency: Utilize parallelism: Identify portions of the code that can be parallelized and leverage concurrent execution, multi-threading, or distributed computing techniques to utilize multiple cores or machines for improved performance. Asynchronous programming: Use asynchronous patterns or frameworks to handle I/O operations efficiently, allowing other tasks to proceed while waiting for results. 5. Caching and Memoization: Caching: Implement caching mechanisms to store frequently accessed or computed data. Caching can reduce the need for repeated expensive computations and improve response times. Memoization: Apply memoization techniques to cache the results of expensive function calls, avoiding recomputation when the same inputs are encountered.

How to build an API

Define the purpose: Determine the purpose and functionality of your API. Identify the specific tasks or data it needs to handle and the problem it aims to solve. This includes deciding on the type of API (e.g., RESTful API, GraphQL) and the resources it will expose. Design the endpoints: Define the endpoints (URLs) that clients will use to interact with your API. Each endpoint represents a specific action or resource. For example, you might have endpoints like /users to fetch a list of users or /products/{id} to retrieve a specific product by its ID. Choose HTTP methods: Determine the appropriate HTTP methods (GET, POST, PUT, DELETE, etc.) for each endpoint. These methods indicate the type of operation the client wants to perform on the resource, such as retrieving data, creating new data, updating existing data, or deleting data. Implement the endpoints: Write the server-side code to handle each endpoint. This involves creating the necessary logic to process incoming requests, interact with databases or other data sources, and generate appropriate responses. Depending on the programming language and framework used, you'll have specific tools and libraries to help you implement the endpoints. Handle request and response formats: Decide on the format of data that clients will send in requests (e.g., JSON, XML) and the format of data the API will return in responses. Implement code to parse and validate incoming requests and format the responses accordingly. Add authentication and security: Implement authentication mechanisms to secure your API and control access to sensitive resources. This may involve using API keys, tokens, or other authentication methods. Additionally, consider implementing security measures such as rate limiting, input validation, and encryption to protect against potential threats. Test and debug: Thoroughly test your API endpoints to ensure they work correctly. Use tools like Postman or cURL to send requests and validate the responses. Check for any bugs or errors and debug them as necessary. Document the API: Create clear and comprehensive documentation for your API. Document each endpoint, its purpose, the required parameters, expected responses, and any additional information that will help developers understand and use your API effectively. Publish and version the API: Publish your API to make it accessible to developers and consumers. Determine how you will handle versioning if there are future changes or updates to the API to maintain backward compatibility. Monitor and maintain: Continuously monitor the performance and usage of your API. Collect metrics, track errors, and make improvements as needed. Maintain and update the API over time to accommodate new features, address issues, and meet changing requirements. Remember that building an API involves considerations beyond these steps, such as scalability, error handling, and performance optimization. However, these simplified steps provide a general outline to get started with building an API.

Software Design Patterns

Formalized best practices that the programmer can use to solve common problems when designing an application or system. Examples include creational, structural, and behavioral.

I/O operations and network requests

I/O operations, or input/output operations, refer to interactions between a computer system and external devices or resources. These operations involve reading from or writing to devices such as disks, keyboards, printers, or network connections. In the context of programming, I/O operations often refer to reading or writing data from/to files, databases, or network resources. For example, reading a file from the hard drive, writing data to a database, or sending/receiving data over a network are all examples of I/O operations. Network requests, on the other hand, specifically refer to communication between a computer and other devices or systems over a network, such as the internet. This involves sending a request from one device (client) to another (server) and receiving a response. Network requests are commonly used in web development, where a client (typically a web browser) sends a request to a server for a web page, data, or any other resource. The server processes the request and sends back a response, which may include HTML content, JSON data, images, or any other desired information. I/O operations and network requests are crucial for interacting with the outside world and exchanging data with external resources. They are essential for tasks such as reading and writing files, accessing databases, communicating with servers, or fetching data from the internet. Managing I/O operations efficiently, especially when they involve waiting for responses, is a key consideration in programming to ensure optimal performance and responsiveness.

Memoization

Memoization is a clever technique used to speed up computations by remembering the results of previous calculations. It's like having a notebook where you write down answers to math problems so that you don't have to solve them again later. Imagine you have a complex function that takes some input and produces a corresponding output. Every time you call this function with the same input, it goes through the same time-consuming calculations to produce the result. Memoization steps in to optimize this process. With memoization, the function remembers the inputs it has already encountered and the corresponding outputs it has computed. It stores them in a cache or memory. So, when you call the function again with the same input, instead of redoing all the calculations, it simply looks up the input in the cache. If the result is already there, it can be quickly retrieved and returned without any additional work. This technique is particularly useful when the function's calculations are expensive or time-consuming, and the same inputs are likely to be used multiple times. By storing and reusing the computed results, memoization saves time and resources, making subsequent function calls much faster. In essence, memoization is like having a notebook that remembers the answers to problems you've already solved, allowing you to skip the calculations and retrieve the results instantly. It's a powerful technique for optimizing computations and improving overall performance.

Software Architectural Patterns

Represents the design decisions related to overall system structure and behavior. Examples include MVC pattern.