Start from scratch by picking one language — C++, Java, or Python — and getting comfortable with its syntax, then move to core data structures in this order: arrays, strings, linked lists, stacks, queues, trees, and graphs. Begin with easy problems and aim for 1–2 problems daily instead of binge-solving on weekends. Focus on understanding why a solution works rather than memorising code, and maintain notes of every new pattern you learn. If you are in college, start by your second year; if you are already working, 4–6 months of consistent evening practice is a realistic runway before placement season.

A practical DSA preparation roadmap runs: arrays and hashing → two pointers and sliding window → stacks and queues → binary search → linked lists → trees and BSTs → heaps → recursion and backtracking → graphs → dynamic programming. Spend most of your time on medium-level problems, since that is where the majority of interview questions sit. For every new pattern, solve 4–5 variations before moving ahead, and revise old problems weekly. Hold off on dynamic programming until recursion and trees feel natural — starting DP too early is the most common reason aspirants burn out.

DSA prep is the structured practice of data structures and algorithms so you can solve unseen problems efficiently within a time limit. Companies test it because there is no better proxy for how you think: whether you can break a problem down, choose the right data structure, reason about time and space complexity, and write clean code under pressure. Unlike theory exams, nothing can be crammed the night before — which is exactly why product companies trust it as a filter, and why consistency over months matters more than raw talent.

Coding interview questions are problems where you apply data structures and algorithms to reach an optimal solution while explaining your thinking out loud. Typical examples include finding a subarray with a given sum, detecting a cycle in a linked list, or counting connected components in a grid. In India, service-based companies usually stick to aptitude and basic programming, while product companies such as Amazon, Google, and Microsoft ask 2–3 medium-to-hard problems across rounds and evaluate your approach, code quality, and communication together.

Follow a fixed method: restate the problem in your own words, run through a small example, state the brute-force solution and its complexity, then find the bottleneck and match it to a known pattern — hashing, two pointers, sliding window, recursion. If you are still stuck, solve a smaller input by hand to spot the structure. Interviewers weight the approach heavily; a brute-force solution discussed confidently scores better than a silent, half-remembered optimal one. Practising this thought process out loud is what converts random problem solving into actual interview readiness.

Self-study works for most people if you follow a structured topic order and stay consistent for several months. A DSA preparation course or mentorship becomes genuinely useful in three situations: you have been solving problems for months without improvement, you cannot figure out which topics to prioritise, or you are preparing alongside a full-time job with limited hours. What separates those who clear interviews is not the course but feedback — having someone who has already cracked these interviews review your approach corrects mistakes that solo practice rarely catches.

Use LeetCode as your primary platform, since most product-company questions map to its problem patterns. Supplement it with GeeksforGeeks for concept explanations and company-wise archives, and HackerRank or InterviewBit if you prefer guided, track-based practice. Two platforms used deeply will always beat five used superficially. Whatever you choose, follow one rule: attempt every problem yourself for at least 30–40 minutes before reading any solution, and re-solve it after a week to confirm the pattern actually stayed with you.

The process usually starts with an online assessment of two coding problems plus a workstyle survey, followed by one or two phone screens focused on DSA. The final loop consists of 3–4 rounds combining coding and algorithm problems with behavioural questions on Amazon's Leadership Principles, and system design for experienced roles. One interviewer in the loop, known as the bar raiser, has explicit veto power over the hiring decision. From online assessment to offer, the process typically spans a few weeks, so build your preparation timeline accordingly.

Prepare on three parallel tracks. First, DSA: focus on Amazon-tagged problems on LeetCode with extra depth in trees, graphs, and dynamic programming, since most coding rounds sit at medium difficulty. Second, Leadership Principles: Amazon weighs behavioural answers almost as much as code, so prepare 8–10 STAR-format stories from your projects and internships. Third, simulate the real thing: mock interviews train you to think aloud, take hints gracefully, and manage the 45-minute clock. If you are working full-time, 8–12 weeks of focused evening preparation is a realistic window.

It is demanding, but very much crackable with the right strategy. Two things make it hard: the depth of DSA expected within a 45-minute round, and the Leadership Principles rounds where rehearsed, generic answers get exposed quickly. The encouraging part is that Amazon's interviews follow recognisable patterns — the same problem categories and behavioural themes repeat consistently. Candidates from tier-3 colleges and service-based backgrounds clear it every year with 5–6 months of structured preparation and a few mock interviews before the actual loop.

Coding rounds revolve around data structures and algorithms — arrays, strings, hashing, two pointers, trees, BSTs, graphs, heaps, greedy, and dynamic programming — usually one or two problems per round that you solve live while explaining your approach. Behavioural rounds map your past experiences to Amazon's 16 Leadership Principles with questions like "tell me about a time you disagreed with your team." For experienced SDE roles, expect additional low-level and high-level system design rounds. Internship assessments stay DSA-heavy with tighter time limits.

Filter LeetCode by the "Amazon" company tag and prioritise the most frequently asked problems — the top 100 covers a large share of what repeats. Organise your practice topic-wise: arrays and hashing first, then two pointers, sliding window, trees, graphs, heaps, and finally dynamic programming. Amazon repeats patterns far more than exact problems, so after every solved question, note the underlying pattern and when to apply it. Solving two timed mediums daily while maintaining a pattern notebook is a routine that consistently works.

Very useful. Recent Amazon SDE interview experience posts show you which topics are currently appearing in online assessments and loops, how rounds are structured, and what the bar raiser tends to probe. Read the latest posts on LeetCode Discuss and GeeksforGeeks rather than years-old ones, since patterns shift. The limitation is that experiences vary by team, level, and luck, and people rarely report the questions they fumbled. Treat these posts as pattern research, then validate your readiness with timed mock interviews instead of treating them as a question bank.

Engineers make this switch from TCS, Infosys, and Wipro every year, and the recipe is fairly standard. Give yourself 8–10 months of DSA preparation alongside your job, targeting medium-level problems. Rewrite your resume around impact and metrics instead of responsibilities and ticket work. Use referrals and LinkedIn outreach aggressively, since cold applications from service-based profiles get filtered fast. Do a few mock interviews before applying to your dream companies — many people convert a mid-size product company first and then use that momentum for Amazon-level targets.

It usually comes down to three fixable issues. First, your resume may not be ATS-friendly — complex formatting and missing keywords get you filtered before a human ever sees it. Second, your projects are likely written as responsibilities ("worked on REST APIs") rather than measurable impact ("reduced API latency by 40%"). Third, you may be applying cold without referrals, which drastically lowers conversion. Fix these with a clean one-page resume, quantified bullets, referrals for every application, and a LinkedIn headline aligned to your target role. If calls still do not come, get your resume reviewed by someone already working at a product-based company — a 10-minute correction often unblocks months of silence.