Start with the core building blocks — load balancers, caching, CDNs, SQL vs NoSQL databases, sharding, replication, message queues, and consistency trade-offs. Then study 12–15 classic designs such as a URL shortener, chat app, news feed, rate limiter, and ticket booking system, and practise drawing the high-level design (HLD) within 35–40 minutes. For product companies and MAANG roles in India, interviewers score how well you justify trade-offs, so narrate your requirements, estimations, and bottleneck analysis out loud. A few mock interviews with experienced engineers will expose gaps that self-study usually misses.

Follow a structured flow: clarify functional and non-functional requirements, estimate scale (users, QPS, storage), propose a high-level design with APIs and data flow, deep-dive into critical components like database schema, caching, and sharding, and finish with bottlenecks, failure handling, and monitoring. Keep talking throughout, because evaluators assess your reasoning, not just the final diagram. The most common mistake is jumping straight to the diagram without clarifying requirements, which reads as weak senior-level thinking.

The most frequently asked problems include designing a URL shortener, an Instagram/Twitter-style news feed, a WhatsApp-style chat system, a video streaming platform, a rate limiter, a notification system, a web crawler, a distributed message queue, and an online movie ticket booking system like BookMyShow. For SDE-2 and senior roles in India, expect at least one dedicated design round with follow-ups on scale and reliability. Solving each problem with the same repeatable framework makes unfamiliar variants much easier to handle.

Yes — System Design Interview by Alex Xu is one of the most widely recommended books for interview preparation. Volume 1 builds the fundamentals with step-by-step designs of common systems, while Volume 2 goes deeper into advanced and real-world systems. Use it to build conceptual clarity, but reinforce it by explaining designs aloud in timed practice sessions, because the book alone does not train the communication skills the round actually tests.

Grokking the System Design Interview is a well-known prep course that teaches system design through solved case studies — URL shortener, chat apps, video streaming, and similar problems — using a repeatable framework. It is useful if you find dense books hard to start with, since it breaks every design into requirements, back-of-envelope estimates, API design, and component deep dives. Pair it with self-driven practice or a mock interview, because a course alone will not prepare you for open-ended, unstructured rounds.

Focus on patterns rather than memorising solutions: arrays and hashing, two pointers, sliding window, binary search, linked lists, stacks and queues, trees, graphs, heaps, and dynamic programming. Solve around 150–250 quality problems, maintain a revision sheet of patterns and mistakes, and time-box every problem before reading the editorial. Product companies and MAANG roles in India expect clean, optimal code with clear complexity analysis, so practise writing production-quality code, not just code that works.

Use a consistent method: restate the problem, clarify constraints and edge cases, explain a brute-force approach, then optimise it while stating time and space complexity before coding. Write clean, readable code, dry-run it on an example, and discuss how you would test it. Thinking aloud matters as much as the final answer — a partially optimal solution with clear reasoning usually scores better than silent struggling.

Entry-level candidates are most often tested on arrays, strings, linked lists, stacks, queues, sorting and searching, recursion, and basic tree and graph problems — classics include Two Sum, Valid Parentheses, reversing a linked list, cycle detection, and level-order traversal. Interviewers for fresher roles in India focus on clarity of fundamentals, the progression from brute force to optimal, and honest complexity analysis. Working through a curated set of frequently asked questions with solutions is far more effective than randomly solving hundreds of problems.

Yes — for candidates with around 3+ years of experience, coding rounds usually shift to medium and hard problems on dynamic programming, graphs, sliding window, and heap-based patterns, often with follow-ups on optimisation and trade-offs. Interviewers also probe depth: why you chose a particular data structure, how the solution scales, and how you would handle edge cases in production. Engineers moving from service-based to product companies in India should budget focused preparation time, since coding rounds carry heavy weight regardless of experience.

Pick one platform such as LeetCode or GeeksforGeeks and solve consistently in Java so the language becomes second nature under interview pressure. Master the Collections Framework — ArrayList, HashMap, HashSet, PriorityQueue, Deque — along with the time complexity of built-in operations, since choosing the right structure is half the optimisation. Practise writing code without IDE autocomplete, and be ready for Java-specific follow-ups such as HashMap internals or equals/hashCode, which interviewers for backend Java roles in India commonly ask.

Model Context Protocol (MCP) is an open standard, introduced by Anthropic, that defines how LLM-powered applications connect to external tools, data sources, and services. It works on a client-server model: an MCP server exposes capabilities such as tools, resources, and prompts, while an MCP client inside the AI application discovers and invokes them over a standardised message format. This removes the need for custom, one-off integrations between every model and every tool, which is why MCP has quickly become a key skill for engineers building agentic and GenAI systems.

MCP servers are lightweight programs that expose a specific capability — a database, GitHub, Slack, a file system, or an internal API — to AI applications through the standard protocol. Each server defines the tools a model can call, the resources it can read, and optional prompt templates, while the client handles discovery automatically. Prebuilt servers are available in public repositories and registries, and backend engineers can write custom MCP servers in Python or TypeScript when off-the-shelf options do not cover their use case.

Start by choosing an MCP-compatible client such as Claude Desktop or your own agent application, then add MCP servers in its configuration — for example, a filesystem server or a database server — and the client will automatically detect the tools those servers expose. Beginners should connect one or two prebuilt servers first to understand how tools, resources, and prompts flow between the client and the model. Once comfortable, learn the JSON-RPC message structure and transport options so you can debug integrations instead of only configuring them.

Implementing MCP usually means building your own server: install the official SDK (Python or TypeScript) from the Model Context Protocol GitHub repository, define your tools with clear input schemas, expose them over stdio or an HTTP-based transport, and test them with an MCP inspector before wiring them into a real application. You can then connect the server to an LLM application using frameworks like LangChain or enterprise platforms such as Azure AI Foundry to power agentic workflows. Start with a simple server exposing two or three tools, then add authentication, streaming, and error handling as your use case matures.

No, there is no official Model Context Protocol certification as of now. The credible way to demonstrate MCP skills is to build and publish working projects — for example, a custom MCP server integrated into an AI agent — with the code available on GitHub, because hiring managers for AI engineering roles in India weigh real implementations far more than certificates. Broader cloud AI certifications can complement this, but hands-on MCP projects remain the strongest proof of skill.