Got maybe 60-70% of test cases passing and ran out of time before fixing the rest.
Start by clarifying the problem and constraints, then define the DP state on the tree (e.g., dp[node][state]) and derive transitions from children to parent. Implement a post-order DFS to compute DP values bottom-up, and analyze time and space complexity.
Pro tip: Always discuss the trade-offs between recursive DFS (clean but risk of stack overflow) and iterative post-order (more complex but safer for deep trees), and mention how you would handle large inputs.
Ask questions to understand the exact problem: input format, tree properties (rooted? binary? weighted?), and what needs to be optimized. Confirm constraints like number of nodes and expected time complexity.
Identify what information each subtree needs to pass to its parent. Define dp[node][state] where state captures necessary conditions (e.g., selected/not selected, color, etc.). Write recurrence relations combining children's DP values.
Use post-order DFS (recursive or iterative) to process children before parent. Implement the DP transitions carefully, handling base cases (leaf nodes) and merging child results.
State time and space complexity (usually O(N * states) time, O(N * states) space). Discuss possible optimizations like reducing state space or using iterative DFS to avoid recursion limits.
Walk through a small example tree to verify the DP transitions and base cases. Consider edge cases like single node, skewed tree, or large branching factor.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and edge cases, then quickly outline a brute-force solution before optimizing. Focus on matrix traversal patterns and space-time trade-offs, and communicate your thought process clearly under time pressure.
Pro tip: Verbally prioritize the most impactful optimization (e.g., reducing time complexity from O(n^2) to O(n)) and mention potential pitfalls like integer overflow or empty matrices. This shows you think about production-quality code, not just correctness.
Ask clarifying questions about matrix dimensions, data types, and expected output. Restate the problem in your own words to confirm understanding.
Briefly describe a naive solution and its complexity. This buys time and sets a baseline for optimization.
Identify matrix traversal patterns (e.g., spiral, diagonal, BFS/DFS) or algebraic properties (e.g., transpose, rotation) to improve efficiency.
Write clean code while handling edge cases like empty matrix, single row/column, or non-square matrices. Use meaningful variable names.
Walk through a small example, test edge cases, and state final time/space complexity. Mention potential improvements if time allowed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scope, then design a modular, scalable food court system with clear components like order management, vendor integration, and payment processing. When the interviewer adds constraints, adapt your design by identifying trade-offs and iterating on the architecture to meet new requirements.
Pro tip: Proactively discuss trade-offs (e.g., consistency vs. availability, latency vs. cost) and how you would monitor and scale the system; this shows senior-level thinking. Also, treat the extension as an opportunity to demonstrate flexibility and deep understanding of distributed systems.
Ask questions to understand functional and non-functional requirements, such as expected scale, user types (customers, vendors, admins), and key features (ordering, payment, notifications).
Sketch the main components (e.g., API gateway, order service, vendor service, payment service, database) and their interactions. Define data models and core APIs.
Choose 1-2 components (e.g., order processing or payment) to detail, discussing data consistency, concurrency, and failure handling.
Explain how to scale (e.g., horizontal scaling, caching, sharding) and ensure reliability (e.g., retries, circuit breakers, idempotency).
When the interviewer adds constraints, revisit your design, identify impacted areas, and propose modifications while discussing trade-offs (e.g., adding real-time order tracking may require WebSockets and increase complexity).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.