Practice by pattern instead of by random company lists—pick one topic like arrays, trees, or dynamic programming, solve a focused set of problems in it, and only then move to the next. Always simulate real conditions: timer on, thinking aloud, no hints until you are genuinely stuck. If you are unsure how to practice coding interview questions in the right order, a pattern-based sequence with spaced revision beats grinding randomly, and you should finish with a few timed mock interviews before you start applying.

Treat every mock like the real round: 35–45 minutes, one problem, camera on, clarifying questions asked upfront, and your thought process narrated throughout. Start with a friend or peer to get comfortable speaking while coding, then do at least a couple of mocks with someone who actually conducts interviews, because calibrated feedback on communication and approach is exactly what solo practice misses. Even two or three serious mocks usually expose gaps that months of quiet problem-solving never reveal.

Compare options on three things: who the interviewer is (a peer, an AI, or a working engineer who takes real interviews), how specific the feedback is, and how closely the format mirrors actual FAANG rounds. Peer platforms and AI tools are useful for warm-ups, but a coding mock interview platform that connects you with engineers who currently interview candidates at product companies gives far more reliable signal before your actual rounds.

An AI coding mock interview is great for high-volume repetition, building speed, and getting past the fear of coding under pressure. What it cannot fully replicate is a human interviewer—follow-up probing, reading your hesitation, and judging how clearly you explain trade-offs. The strongest preparation uses AI mocks early on and switches to sessions with real interviewers, ideally engineers who conduct interviews at product companies, in the final weeks.

Free mocks with friends or peer-matching communities are a good starting point, especially if you get nervous speaking while you code. The limitation of a free coding mock interview is feedback quality—peers rarely know what real interviewers are listening for. Use free sessions to build comfort, then book one or two rounds with an experienced interviewer closer to your interview date to fix calibration issues like pacing, clarifying questions, and how you handle hints.

It depends on how much structure you already have. Courses help most if you struggle with consistency or do not know what to study in what order; they add little if you are already disciplined with DSA practice. Before committing to an expensive program, check whether your actual gap is narrower—a personalised placement roadmap, a few mock interviews, or a resume review—since targeted 1:1 mentoring often fixes specific weaknesses faster and at lower cost.

A practical sequence is roughly 8–10 weeks of pattern-based DSA, system design basics layered in for experienced candidates, behavioural prep using the STAR method, and mock interviews spaced through the final month. Freshers managing college placements should add aptitude and core CS subjects like OS, DBMS, and OOP early on. Since the right plan depends on your current level, target companies, and timeline, a placement roadmap built for your situation works far better than copying a generic one.

Freshers are tested mainly on DSA, CS fundamentals, and potential, while experienced candidates are assessed on depth—system design, decisions made in past projects, and trade-off reasoning. That means FAANG interview preparation for experienced engineers should weight system design, project walkthroughs, and behavioural rounds much more heavily, because interviewers will dig into your real work history in detail.

First decide your route—on-campus, off-campus product companies, or both—because hiring timelines in India differ sharply between them. Build depth in one language, pattern-based DSA, and core CS subjects before touching company-specific question lists. FAANG interview preparation in India also means planning around service-company joining dates and off-campus drive windows, so get your resume shortlist-ready before the season starts instead of fixing it after rejections pile up.

Curated GitHub repositories are excellent as a question bank—problem lists by pattern, interview-experience notes, and revision sheets all live there. But FAANG interview preparation on GitHub is one-directional: it cannot hear your explanation, catch vague communication, or pressure-test your approach the way a mock with a real interviewer can. Treat repositories as your syllabus and add timed mock interviews on top of them.

If you are getting automated rejections despite relevant skills, it usually is—most software engineering resumes fail on presentation rather than qualifications: weak bullets, no metrics, skills buried, or a format recruiters skim past. A proper software engineer resume review service gives you specific, experience-based fixes instead of generic tips, which matters most when a recruiter spends only seconds deciding on your application. If your resume is already converting into interview calls, spend that money elsewhere.

Look for resumes shared by candidates who actually got shortlisted at product companies—through GitHub repositories, career communities, and mentor pages—rather than random template sites. When you study software engineer resume examples, copy the structure, not the content: one page, projects with measurable outcomes, and skills matched to each job description. Examples show you the format, but only a review of your own resume catches your specific weak points.

Keep it to two or three lines: your role and experience level, your strongest stack or domain, and one concrete proof point—an internship, a shipped project, or a competitive achievement. A software engineer resume summary should be tailored to each application, never a recycled objective statement; freshers should lead with projects and internships while experienced candidates should lead with impact and scale. If the lines could sit on anyone's resume, rewrite them.

The most effective route is a genuine connection: reach out to Amazon engineers through LinkedIn, alumni networks, or mentorship platforms with a short, specific message that includes your resume and the role ID. A referral works only when your profile actually matches the opening, so it is smart to get your resume reviewed and your fit assessed first. Some mentors who work at Amazon and regularly take interviews there offer dedicated referral sessions where they check whether your skills align with open roles before referring you.