Begin with a realistic timeline (3–6 months) and lock in one programming language you'll stick with. Build DSA fundamentals first — arrays, strings, hashing, linked lists, trees, graphs and dynamic programming — then move to curated problem sets on platforms like LeetCode and GeeksforGeeks. Add mock interviews and STAR-format behavioural stories in the last 4–6 weeks. If you're employed, plan applications around notice periods, since hiring at product companies and FAANG offices in India often follows structured cycles.

A practical split: Months 1–2 for DSA foundations and easy–medium problems, Months 3–4 for advanced patterns, graphs and dynamic programming with timed practice, Month 5 for system design (if you have 3+ years of experience) and behavioural preparation, and the final month for mock interviews and revision. Around 8–10 focused hours a week is sustainable alongside a full-time job. Stretch or compress the timeline based on how strong your fundamentals already are.

For experienced candidates, interviews go beyond DSA — system design becomes a deciding round, behavioural rounds carry more weight, and your resume is probed deeply for ownership and impact. Expect 4–6 months of preparation: revise DSA at a medium–hard level, practise designing scalable systems with clear trade-offs, and prepare 8–10 detailed project stories with measurable outcomes. At senior levels, depth and clarity of decision-making matter more than raw speed.

It depends on where you're starting from. A structured course helps if you need discipline, direction, or a fundamentals refresher, but courses alone rarely get anyone selected — consistent problem-solving and personalised feedback matter far more. Many candidates in India combine self-practice with 1:1 mock interviews or mentorship, because feedback from someone who has actually sat on the hiring side is something recorded courses and YouTube playlists can't replicate.

Start with one language (Python, Java or C++), then learn the core structures — arrays, strings, linked lists, stacks, queues, hashmaps, trees, heaps and graphs — before moving to patterns like two pointers, sliding window, recursion and dynamic programming. Solve topic-wise problems instead of random ones, aim for consistency (1–2 problems daily) over intensity, and revisit problems you failed after a week. Free platforms like LeetCode and GeeksforGeeks are more than enough to begin.

Yes. Python is accepted in coding interviews at virtually every company, including FAANG, and its concise syntax lets you focus on logic instead of boilerplate. Choose it if you're already comfortable with it; if you're starting fresh, Python has a gentler learning curve than C++ or Java. Just make sure you understand what built-in helpers like sort or Counter are doing under the hood, since interviewers may ask you to implement the logic yourself.

Exact questions vary, but patterns repeat heavily: hashing problems like two sum, sliding window, binary search, linked-list reversal and cycle detection, tree traversals and BST problems, BFS/DFS and topological sort on graphs, and dynamic programming classics like coin change, longest increasing subsequence and knapsack. Covering these 8–10 patterns with 15–20 problems each prepares you for the large majority of data structures and algorithms interview questions at product companies.

Practice topic-wise rather than randomly: pick one pattern, solve 10–15 problems on it, then move on. Time-box every problem to 30–40 minutes, attempt it fully before looking at solutions, and re-solve the ones you failed after a few days. Say your approach out loud while coding — in real interviews, communication is evaluated as much as the final answer. Depth of practice beats the number of problems solved.

Fresher rounds usually focus on arrays, strings, sorting and searching, recursion, linked lists and basic dynamic programming, mostly at the easy–medium level. In India, product-based companies also test CS fundamentals — OOP, OS, DBMS and networks — alongside the coding round, while service-based companies typically add aptitude tests. Practising previous year questions from the companies you're targeting is one of the highest-ROI things a fresher can do.

Yes — almost every company, including FAANG, lets you choose your interview language, and Java is one of the most commonly used choices in India. Be thorough with the Collections framework (HashMap, ArrayList, PriorityQueue), since these come up constantly. One caution: don't lean on library shortcuts when the question is specifically testing that logic — know what you can use off the shelf and what you're expected to build yourself.

There's no single "best" one, but Data Structures and Algorithms Made Easy by Narasimha Karumanchi is the most widely used interview-focused data structures and algorithms book in India, especially for campus placements and service-to-product switches. Pair it with an online problem bank like LeetCode for hands-on practice, and reach for CLRS only if you want deep theoretical grounding. One book plus consistent problem-solving works far better than collecting multiple books.

Technical interview questions test how you think and solve problems, not just what you've memorised. They typically cover coding and DSA, CS fundamentals (OOP, OS, DBMS, networks), system design for experienced roles, and sometimes deep-dives into your projects. They're different from HR or behavioural rounds, which assess culture fit and communication — at most product companies in India, you'll face 2–4 technical rounds before those.

Most successful candidates complete 5–10 realistic mock interviews before the actual round — enough to manage nerves, handle time pressure, and get used to thinking out loud. Spread them across the final 4–6 weeks, covering DSA rounds, system design if it applies to your level, and behavioural questions. The feedback from each mock — where you got stuck, how you communicated — is worth more than the count itself.

Feeling stuck while performing well is one of the most common patterns among engineers with 5–10 years of experience. It usually comes from doing the same category of work for too long, building skills without a direction, or chasing promotions without clarity on what you actually want. A practical fix: audit your skills against the roles you want, get honest feedback from someone senior, and set a specific 12-month target — clarity usually comes from structure, not from grinding harder.

Yes — before anyone sees you code, your resume decides whether you get shortlisted. Recruiters spend under 30 seconds on it, so impact-driven, quantified bullets ("reduced latency by 40%") beat responsibility lists, and clean formatting with role-relevant keywords matters. A strong referral combined with a sharp resume dramatically improves shortlist odds at FAANG-level companies; after that, your interview preparation takes over.