Practice consistently rather than in bursts — solving 1–2 quality problems daily beats solving 20 on weekends. Pick one language, master the core DSA patterns (arrays, strings, hashmaps, two pointers, trees, graphs, dynamic programming), and always note the time and space complexity after every solution. Time-box yourself to 25–35 minutes per problem to simulate real pressure, and re-solve problems you failed after a few days. In the final weeks before applying, convert this practice into full mock interviews so you train communication, not just problem-solving.

Treat a mock exactly like the real thing: a 45–60 minute session where the interviewer gives you one problem and you think aloud, clarify requirements, discuss a brute-force approach, and then optimize. The other person should stay mostly silent, drop hints only when you're stuck, and probe your complexity analysis and edge cases at the end. You can do this with a friend, a peer-matching platform, or by booking a session with an experienced interviewer. The most valuable part is the 10 minutes of honest feedback afterwards — on communication, approach, and where you froze — because that's what solo practice never gives you.

A good mock mirrors the company you're targeting. For most product companies, that means one or two LeetCode medium-level problems — arrays, strings, hashmaps, sliding window, trees, or graphs — with follow-ups on optimization and edge cases. Experienced candidates should also expect harder dynamic programming or design-flavoured follow-ups. If your mock only contains problems you've already solved comfortably, it isn't testing anything, so ask the interviewer to pick unfamiliar problems.

There is no single best one — it depends on what you need. Free peer-matching platforms are great for cheap repetitions and getting used to thinking aloud; mock coding interview AI tools help you squeeze in extra practice anytime; and paid 1:1 mock interviews with engineers who have actually sat on the interviewer's side give the most realistic calibration, because they know how real interviewers evaluate and score. A sensible mix is several free or AI mocks early on, then one or two sessions with an experienced interviewer just before your real interviews.

AI tools are genuinely useful — they're available anytime, give instant feedback, and remove the awkwardness of your first few attempts. What they can't replicate is human interviewer behaviour: interrupting you mid-solution, giving vague hints, applying pressure, and judging communication the way a real panel does. Use mock coding interview AI tools for high-volume practice, but keep a few human mocks in your plan, especially in the last weeks before your interview.

A practical roadmap runs in phases over 3–6 months: first rebuild DSA fundamentals (arrays, strings, hashmaps, linked lists, stacks, trees), then move to patterns like two pointers, sliding window, BFS/DFS, backtracking, and dynamic programming while solving mediums consistently. Experienced candidates should add system design — scalability, databases, caching, API design — in parallel. The final month should go to company-specific practice, behavioural stories in STAR format, and full mock interviews. The most common mistake is spending 90% of the time on tutorials and never doing a timed mock.

For most working professionals and students, 4–6 months of consistent preparation is realistic; 3 months works only if your fundamentals are already strong. In India, the timeline also has to fit around off-campus hiring cycles, referral-driven applications, and college placement season, so plan backwards from when applications open rather than from the interview date. Whatever the duration, daily consistent problem-solving beats weekend marathons, and the last 3–4 weeks should be reserved for mocks and company-specific questions.

It depends on how you learn. If you're self-disciplined, free resources, consistent problem practice, and mocks are enough — plenty of people crack FAANG without any paid course. A course makes sense if you need structure, deadlines, and someone to unblock you when you're stuck. If you do pay for one, judge it on whether it includes real mock interviews and personalised feedback, not the number of recorded lectures, because videos alone rarely change outcomes.

The coding rounds don't go away — you will still be tested on DSA — but the bar shifts. Experienced candidates face heavier system design rounds (scalability, trade-offs, data modelling, API design), deep project discussions where interviewers probe your actual ownership and impact, and behavioural rounds that assess leadership. You usually need less grinding from scratch but far more depth: interviewers expect you to justify decisions like a senior engineer. A focused 3–4 month plan centred on design and polish usually works better than a beginner-style 6-month grind.

Paste your resume along with the exact job description and ask it to act as a hiring manager for that role — then request specific outputs: missing keywords for ATS, weak bullets without metrics, unclear impact, and formatting problems. A useful follow-up is asking it to rewrite your three weakest bullets with quantified impact, or to simulate the ten questions a recruiter would ask after reading your resume. One caveat: it cannot know what real interviewers at your target companies respond to, so treat its output as a strong first pass and get a final review from someone who has actually hired for such roles.

Sometimes — it depends entirely on who is reviewing. On LinkedIn, resume review usually means a coach or experienced professional goes through your resume line by line, often on a call, which is far more valuable than an automated tool when the reviewer has actually interviewed or hired for roles like yours. It's worth paying when you're not getting interview calls despite solid experience, because the problem is usually how your work is presented, not your skills. It's not worth paying for generic checklist feedback, so always verify the reviewer's background first.

Blind resume review means the evaluator sees your resume with identifying details — name, college, gender, sometimes even company names — removed, so the feedback is based purely on content: skills, impact, clarity, and formatting. The goal is to remove bias and find out whether the resume would actually clear a screen on its own merit. Some communities and services offer it, and you can approximate it yourself by asking a senior to review an anonymised version so they judge the writing rather than your pedigree.

It depends on who reviews it. Automated tools and AI return feedback in minutes; a thorough human reviewer typically takes 2–5 days; and a live 1:1 review call gives you feedback within the hour, along with the reasoning behind every change. If you have an application deadline, a live or same-day review is the safer option — and always budget time afterwards to actually implement the feedback instead of sending out the same version.

Free options include asking seniors or friends who work at your target companies, posting an anonymised version in job-seeker communities on Reddit, and running your resume through AI tools with a well-written prompt. The catch is that free feedback is usually generic — "add metrics", "keep it to one page" — and rarely explains why recruiters at product companies are skipping your profile. A free resume review is a solid first pass; if you've been rejected repeatedly despite relevant experience, one detailed review from someone who has hired for those roles usually pays for itself.

Three things: the reviewer's background, the depth of feedback, and whether it is tailored to your target role. The most useful reviews come from people who have actually interviewed or hired for the companies you're applying to, because they know what screening looks like from the other side. Look for line-by-line comments, rewritten bullets with quantified impact, ATS and formatting checks, and an honest verdict rather than just a score. A 30–45 minute live review where you can ask questions is worth far more than an annotated PDF of generic tips.