Most people overcomplicate how to prepare for an SDE interview — it comes down to four buckets: DSA patterns (arrays, hashing, trees, graphs, DP), CS fundamentals (OS, DBMS, networks), depth on the projects listed in your resume, and communication under pressure. Practice solving problems while thinking out loud, because silent coding is one of the most common reasons strong candidates fail rounds. Keep the last two weeks for mock interviews and company-specific research instead of starting new topics.

If you're figuring out how to prepare for an Amazon SDE interview, work on two parallel tracks: DSA (graphs, trees, DP, heap-based problems) with clean, bug-free code, and the Leadership Principles — prepare two to three STAR-format stories for each one from real work. Behavioral rounds at Amazon carry as much weight as coding rounds, so don't leave them for the last day. If you're applying for SDE-2 or above, add proper system design (HLD) practice, and prioritise referrals since online applications alone rarely get screened.

People overthink how to prepare for a Google SDE interview — it rewards depth, clarity, and collaboration rather than memorised solutions. Focus on core DSA patterns with full complexity discussions, edge-case handling, and talking through trade-offs out loud; for experienced roles, add system design. Practise writing code in a plain document without autocomplete, and prepare specific stories around ambiguity, teamwork, and failure for the behavioural round.

The biggest shift in how to prepare for an SDE 2 interview is that the bar moves from solving problems to designing systems and showing ownership. Alongside tougher DSA rounds, expect deep HLD discussions — scaling, trade-offs, data modelling, API design — and behavioural rounds that probe end-to-end ownership. Prepare three or four detailed stories about modules you drove, decisions you made, and measurable impact, because that's what separates SDE-1 profiles from SDE-2 offers.

A realistic SDE interview preparation roadmap for 8–12 weeks looks like this: weeks 1–4 on DSA patterns with daily problem-solving, weeks 5–6 on CS fundamentals plus system design if you're experienced, week 7 on your own resume projects and behavioural stories, and the final stretch on mock interviews and company-specific prep. Freshers should weight DSA and fundamentals heavily, while experienced engineers need far more system design depth. Review weak areas weekly instead of solving random problems and hoping for the best.

An SDE interview preparation course is worth it only when it gives you structure, accountability, and real feedback — most of the core content like DSA patterns and system design basics is already available for free. If you're self-disciplined, a solid roadmap plus consistent mock interviews will beat any recorded course. If you struggle with consistency or don't know what product companies actually expect, pick something with human feedback and mocks, and judge it on feedback quality rather than the number of lecture hours.

The core of how to review a resume comes down to five checks: every bullet shows measurable impact instead of responsibilities; the resume is tailored to the specific job description instead of one generic version; projects clearly state your individual contribution; it can be skimmed in seconds and the key signal still lands; and there are no buzzwords without evidence behind them. If your resume reads like a job description of your past roles rather than proof of outcomes, fix that first — that's why many otherwise strong profiles get screened out.

Paste your resume along with the job description of the role you're targeting, and use a resume review prompt that asks ChatGPT to act as the hiring manager for that exact role. Ask it to flag weak bullets, missing metrics, unclear impact, and keyword gaps against the JD, then rewrite your top five bullets based on that feedback. Treat it as a strong first pass on structure and wording — AI can't tell you how a recruiter at a specific product company will read your profile, so follow it with a human review before you start applying.

An AI resume review is good for grammar, formatting, ATS keywords, and tightening bullets, but it doesn't know why strong profiles still get rejected, which projects a hiring manager actually values for a role, or how your resume compares with the rest of the pipeline. Use AI for a clean first pass, and if you're applying consistently without callbacks, get the resume checked from the hiring side — that's usually where the real gap shows up.

For a free resume review, your realistic options are ATS-scanner tools, hiring-side connections on LinkedIn, mentorship communities, Reddit threads, and college placement cells. The catch is that most free feedback stops at generic advice — one page, add metrics, fix fonts — without context on what recruiters in your target companies look for. A free review is fine for a first cleanup, but if you're targeting product companies and getting no callbacks, feedback from someone who has actually hired for those roles is worth far more than another formatting pass.

A resume review service is worth paying for only when the reviewer has real hiring context — otherwise you're paying for template rewrites and grammar fixes you can get for free. Before paying, check exactly what you'll receive: a marked-up resume with specific fixes, gaps against your target role, and a short discussion, not just a rewritten document. If you have relevant experience but aren't getting callbacks, a review from someone who hires for those roles usually pays for itself quickly.

Resume review on LinkedIn means getting your resume evaluated by recruiters, hiring managers, or career experts you find through LinkedIn — via their listed services, posts offering reviews, or a direct request. If you're unsure how to review a resume on LinkedIn, the practical route is to shortlist people who actually hire in your domain, send a polite message with your resume and one specific ask (for example, why you're not getting callbacks for SDE roles), and keep the request easy to act on. Specific, focused asks get replies; open-ended "please review my resume" messages usually don't.

A CV review is a structured evaluation of your CV by someone who understands hiring — checking structure, relevance to the target role, measurable impact, and how quickly a recruiter can find the signal in it. In India, "CV" and "resume" are used interchangeably, so a CV review and a resume review are effectively the same thing. Get one done when you're about to apply in bulk, switching from service-based to product companies, or getting rejections without feedback — that's when a review changes outcomes instead of just polishing formatting.

Tech career counseling is worth it when you're at a decision point — moving from service-based to product companies, choosing between offers, deciding whether to switch roles or upskill, or feeling stuck without knowing why. A good counsellor gives you clarity on trade-offs from a hiring-market perspective, not generic motivation or boilerplate advice. If your only problem is a weak resume or interview performance, go straight to those specific fixes instead; counseling pays off when the confusion is about direction, not execution.

Tech career advice on Reddit is genuinely useful for raw signal — real interview experiences, salary transparency, and honest takes on companies that you won't get elsewhere. The limitations are that it's scattered, often US-centric, and heavily skewed by survivorship bias: the person saying "just grind 500 LeetCode problems" knows nothing about your background, target companies, or timeline. Use Reddit to understand how processes actually work and to shortlist your doubts, then validate the final decisions with someone who can look at your specific profile — that combination beats relying on either one alone.