Interview Process & Prep

The interview is conducted entirely in English. It is not particularly difficult if you prepare properly. Study technical definitions and practice live coding exercises. Practice explaining your step-by-step thought process out loud in English. Common topics covered include: general experience and challenges, programming principles (SOLID, YAGNI, Dependency Injection), OOP vs Functional Programming, system design, Monolith vs Microservices architecture, web security (XSS, Clickjacking, token storage), performance optimization (bundle size, tree shaking, lazy loading), testing concepts (unit testing, E2E testing, integration testing, TDD), JavaScript fundamentals (variables, hoisting, closures, async/await, event loop, memory leaks), TypeScript (type narrowing, generics, utility types, interface vs type alias), and React (hooks, optimization, reconciliation, SSR, portals, Higher-Order Components).

At Amazon, the technical interview difficulty scales with the level. SDE I (Junior) interviews are relatively straightforward and focus on fundamentals such as data structures and algorithms. SDE II (Mid-level) adds architecture and system design questions. SDE III (Senior) interviews are significantly more challenging, with deeper emphasis on system architecture and complex problem-solving.

The typical wait time is approximately two weeks. If after that period you have not received any communication, it is likely that the company is no longer considering you for the position.

In theory, AWS certifications can add value to a profile, but in practice many positions are obtained without holding one. Their weight varies by company: some organizations place significant importance on certifications, while others focus primarily on hands-on experience and practical skills. A solid understanding of AWS concepts and real-world usage is generally considered more important than the certification itself for most roles.

A well-rounded answer should cover the following points: use AI tools (such as Claude, Cursor, or similar assistants) primarily for repetitive or easily reviewable tasks, such as generating mocks, DTOs, or small self-contained modules. Highlight that AI accelerates documentation writing and test generation. You can mention applying specification-driven development (SDD) approaches to produce artifacts like design guides, PRDs, or test plans. For roles that specifically require AI expertise, you may elaborate on more advanced usage such as orchestrating agents that delegate work to sub-agents. Always tailor the depth of your answer to the role: if AI is not a stated requirement, a brief, practical answer focused on productivity gains is sufficient and tends to satisfy interviewers.

The most common types of live coding interviews are bug fixing and algorithm challenges. Algorithm problems tend to be the most frequently encountered format.

A Coderbyte live challenge typically includes theoretical multiple-choice questions followed by coding exercises. The coding exercises may cover algorithms, JavaScript, React, or other technologies depending on the company's requirements. There are many example videos available on YouTube to help you prepare.

The number of handshakes is x(x-1)/2. This is a combinatorics problem equivalent to choosing 2 people from x (C(x,2)). For example, if 5 people meet, there are 5×4/2 = 10 handshakes.

Big O notation describes the performance characteristics of an algorithm in terms of time and space complexity. It represents the balance between how much memory and how much time an algorithm requires as the input grows. In technical interviews, being able to explain how your solution improves performance—for example, optimizing from O(n²) to O(n)—demonstrates strong problem-solving skills and scores points with interviewers. Generally, optimizing for time (reducing iterations) is prioritized over optimizing for memory. The goal is to identify which solution is more efficient and performs fewer operations.

The recommended approach is to configure a separate SSH key for each account (using the corresponding email), add each public key to its respective GitHub account, and set up both accounts in your ~/.ssh/config file. Then, clone each repository using the SSH URL associated with the correct account. After cloning, configure the Git user locally per repository with: git config user.name "work-username" git config user.email "work-email@example.com" You can verify the connection with ssh -T git@github.com. GitHub identifies you based on the SSH key, not the local username. Avoid cloning via HTTPS, as Git may use cached credentials from a different account and push with the wrong identity.

This problem can be modeled as a DAG traversal. A Depth-First Search (DFS) approach works well: for each service, recursively compute the total time by adding its own duration to the maximum time among its dependencies. Track the path that yields the greatest cumulative duration. Example in Python: python services = { "db": {"dur": 5, "deps": []}, "cache": {"dur": 2, "deps": []}, "api": {"dur": 3, "deps": ["db", "cache"]}, "gateway": {"dur": 4, "deps": ["api"]} } def FindCriticalPath(services): time = 0 bottleNeck = [] def getTimeOfService(name, services, path): serv = services[name] duration = serv['dur'] if len(serv['deps']) == 0: return duration maxTime = 0 maxDep = '' for d in serv['deps']: tempTime = getTimeOfService(d, services, path) if tempTime > maxTime: maxTime = tempTime maxDep = d path.append(maxDep) return duration + maxTime for key in services: path = [] tempTime = getTimeOfService(key, services, path) path.append(key) if tempTime > time: time = tempTime bottleNeck = path return (time, bottleNeck) For the example above, the critical path is gateway → api → db with a total duration of 12 (5 + 3 + 4). The 'cache' service (duration 2) runs in parallel with 'db' (duration 5), so only the longer dependency counts.

Yes, live coding challenges (e.g., on platforms like HackerRank) are a common component of technical interviews. It is advisable to ask the recruiter or HR contact in advance about the specific format of the technical assessment. The night before the interview, practicing algorithm and coding problems on HackerRank or similar platforms is strongly recommended to sharpen problem-solving skills and reduce anxiety during the actual test.

Live coding exercises vary across interviews, but recurring patterns include: algorithmic problems such as the Fibonacci sequence, finding the shortest path to a node in a graph, and string manipulation (e.g., counting how many times a given word can be constructed from a string). For React-focused rounds, typical tasks include implementing a list component, building infinite scroll, debugging an existing component, refactoring a component to separate responsibilities, and optimizing to prevent unnecessary re-renders. API consumption with server-side pagination is also tested frequently.

Make sure you have two computers available in case of technical issues. Before the interview, try the platform using its official tutorials so you are comfortable with the interface. Depending on the difficulty level expected, review the most common algorithms and practice a few medium-level exercises of each type. Sites like algo.monster can help you cover the most frequent problem categories.

One practical approach is to use an AI tool (such as ChatGPT) to generate a large set of practice questions relevant to the technology and role. Working through approximately 50 questions in advance can provide broad coverage of the topics likely to appear in a real interview.

Algorithmic thinking is the primary skill being assessed. Interviewers want to understand how you analyze the problem, how you break it down, and how you reason your way toward a solution — not just whether the final answer is correct. The thought process matters more than the solution itself. In practice, many live coding sessions provide pre-structured or partially complete code that you only need to modify in specific sections, rather than writing everything from scratch. It is important to verbalize your reasoning out loud as you work through the problem.