Preparation for a FAANG interview rests on three pillars: data structures and algorithms (the biggest part of most interview loops in India), system design for experienced roles, and behavioral answers built on STAR stories. Pick one curated problem list and solve it consistently instead of jumping between random problems, maintain a mistake log, and add mock interviews in the final 4–6 weeks. Consistency over 3–4 months beats last-minute cramming.

Most people need 3–6 months of consistent preparation alongside a full-time job, or 2–3 months if preparing full-time. Freshers with strong DSA fundamentals may need less, while anyone switching from service-based companies usually needs extra time for DSA depth and system design. Use your mock interview performance, not the calendar, as the real readiness signal.

A practical FAANG interview preparation roadmap looks like this: months 1–2 for core DSA (arrays, strings, hashmaps, trees, graphs, dynamic programming) with 150–200 curated problems; month 3 for system design fundamentals plus low-level design if you are experienced; and the final month for behavioral stories plus 5–6 mock interviews. Revisit your failed problems weekly instead of chasing question count.

It helps if you need structure, accountability, or don't know what to study — otherwise a disciplined self-study plan with free curated lists covers the same ground. Before paying for any FAANG interview preparation course or bootcamp, check three things: whether it includes live mock interviews, whether feedback is personalised, and whether instructors have actually cleared these interviews themselves. Courses without mock practice rarely justify their price.

No single FAANG interview preparation book covers everything, so pair your resources: start with Cracking the Coding Interview for fundamentals, move to Elements of Programming Interviews for tougher, interview-grade problems, and add a dedicated system design guide once your coding is consistent. Solving and reviewing problems matters far more than reading any book cover to cover.

Yes, as a supplement. FAANG interview prep on Reddit gives you real interview experiences, company-specific round patterns, and honest prep timelines that official sources never share. The catch is that the advice is anecdotal and swings between overly negative and overconfident — use it for direction and realistic expectations, but follow a structured plan for the actual preparation.

Indian interview loops are heavily DSA-focused even for experienced candidates, so make algorithm practice your first priority. Because competition is intense, start collecting referrals early by connecting with engineers in Indian offices on LinkedIn or through mentorship platforms instead of cold-applying. Also plan around the standard 60–90 day notice period so your FAANG interview prep in India is nearly complete before applications go out.

A mock interview is a timed rehearsal of a real interview, complete with follow-up questions and structured feedback at the end. It recreates pressure that silent self-practice never does — thinking aloud, handling hints, and explaining your approach clearly while coding. For FAANG aspirants, it is the most reliable way to find out whether your preparation holds up under stress.

A mock interview with AI is excellent for volume — unlimited reps, flexible timing, and instant feedback on your code and approach at little or no cost. What it cannot fully replicate is interviewer pressure, interruptions, vague follow-ups, and human judgment of your communication. The strongest setup is daily AI practice plus a few sessions with experienced engineers from target companies before your real interviews.

Most candidates see a clear jump in performance after 5–8 full-length mocks spread across the final month of preparation. Quality matters more than count: every session of mock interview practice should end with written feedback and specific fixes for the next one. You are ready when you can solve an unseen medium-level problem while narrating your thought process without going silent.

Realistic options include peer mocks with friends targeting the same companies, college alumni who have recently been through the process, free platforms that match you with strangers for practice, and AI tools. Free mock interview practice is great for building fluency and confidence, but the feedback is rarely calibrated to an actual FAANG bar — mix in at least one or two sessions with someone who has interviewed or hired at that level.

Fresher rounds usually test your self-introduction, deep questions on your resume projects, basic DSA like arrays, strings and hashmaps, plus OOP and CS fundamentals, often ending with HR-style questions. In a mock interview for freshers, expect more project grilling than exotic algorithms, so prepare to explain what you built, why you built it, and what you would improve. Rehearsing this out loud at least twice makes a visible difference in the real interview.

Paste the job description along with your resume and use a detailed resume review prompt — ask it to check for impact-driven bullets (action verb plus measurable result), keyword alignment with the role, formatting and ATS issues, and anything irrelevant to the target job. Then iterate: have it rewrite the weakest bullets and critique its own output. Keep in mind that AI catches structure and clarity problems well but cannot judge whether your experience meets the bar for your target level, so follow up with one expert review.

The best free resume review options are dedicated resume-feedback communities on Reddit, swapping feedback with friends who are getting interview calls, your college placement cell, and AI tools for a quick first pass. Free feedback usually tells you what is wrong but rarely how to fix it for your specific target role — if you have applied to dozens of positions with no calls, it is time for a detailed review from someone experienced in that hiring process.

It is worth paying when the reviewer has actually interviewed or hired at the companies you are targeting, because they know exactly what recruiters and hiring screens filter for. A good resume review service rewrites bullets for impact, removes filler, aligns keywords with the roles you want, and explains the reasoning behind every change. If all you get back is formatting corrections, you have overpaid.