Fully solved the harder one, had some test case failures on the easier one which was embarrassing in hindsight.
Start by thoroughly understanding both problems and their constraints, then prioritize the easier one to secure partial credit quickly. For each problem, design an algorithm, analyze its time and space complexity, and implement it with clean, modular code. Test with provided examples and edge cases before moving on, and if time permits, optimize the harder problem.
Pro tip: In a Google assessment, always verbalize your thought process and trade-offs as if in a real interview; this demonstrates structured problem-solving and can earn partial credit even if the code isn't perfect.
Read both problems carefully, note constraints and examples, and decide which to tackle first based on perceived difficulty and time allocation.
For the chosen problem, outline a step-by-step approach, considering data structures and algorithmic paradigms (e.g., greedy, DP, two-pointer) that fit the constraints.
Determine the time and space complexity of your proposed solution and verify it meets the problem's requirements; if not, iterate on the design.
Write clean, modular code, then test with the provided examples and additional edge cases (e.g., empty input, large values) to catch bugs early.
If time remains, revisit the harder problem or optimize your current solution, and ensure all code is free of syntax errors and handles edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The spreadsheet thing threw me off more than I expected.
Clarify the problem constraints and edge cases, then outline a high-level algorithm before writing any code. Use the shared spreadsheet to write clean, syntactically correct code with meaningful variable names, and test with a few examples to verify correctness.
Pro tip: While coding, verbalize your thought process and trade-offs to demonstrate problem-solving skills, and proactively test edge cases like empty input or large values to show thoroughness.
Ask clarifying questions about input format, constraints, expected output, and edge cases. Confirm assumptions with the interviewer before proceeding.
Describe your approach in plain English, including data structures and time/space complexity. Consider alternative solutions and justify your choice.
Write the solution in the shared spreadsheet, ensuring correct syntax, proper indentation, and clear variable names. Handle edge cases explicitly.
Walk through your code with a simple example, then test edge cases. If bugs are found, debug systematically and explain the fix.
Summarize the solution, discuss potential optimizations or trade-offs, and confirm the final answer meets all requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to structure your answer, focusing on a specific situation where you faced ambiguity or a workplace challenge. Highlight how you adapted, collaborated with your team, and drove a positive outcome, emphasizing your problem-solving and communication skills.
Pro tip: Show self-awareness by briefly reflecting on what you learned and how you would apply it in future scenarios, demonstrating growth and maturity.
Briefly describe the situation, including the project, team dynamics, and the ambiguity or challenge you faced. Keep it concise to focus on your actions.
Clearly state the specific problem or ambiguity, such as unclear requirements, shifting priorities, or team conflict, and why it was important to address.
Explain the steps you took to navigate the situation, emphasizing your thought process, collaboration, and adaptability. Use 'I' statements to highlight your contributions.
Detail the results of your actions, including any metrics, feedback, or impact on the project and team. Quantify if possible.
Summarize what you learned from the experience and how it has influenced your approach to similar situations in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Fumbled the first ten minutes pretty badly.
Start by clarifying the problem constraints and edge cases, then define the DP state and recurrence relation before coding. Implement the solution iteratively, optimizing space if possible, and test with small examples to verify correctness.
Pro tip: Communicate your thought process clearly and involve the interviewer; they care more about your problem-solving approach than just the final code.
Ask clarifying questions to confirm the grid size, movement rules, obstacles, and what constitutes a valid path. Identify edge cases like empty grid or blocked start/end.
Determine what each DP cell represents (e.g., number of ways to reach a cell) and derive the recurrence relation based on allowed moves. Consider if the problem requires min/max cost or counting paths.
Code the DP iteratively, using a 1D or 2D array as needed. Optimize space by rolling arrays if only previous row/column is needed. Handle base cases and obstacles.
Walk through small examples manually, check edge cases, and compare with brute force if possible. Discuss time and space complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and constraints, then implement a clean, general solution for the base binary tree problem. For each follow-up, explicitly identify what changes (e.g., different traversal, additional state, altered constraints) and adapt your approach incrementally, discussing trade-offs and testing edge cases.
Pro tip: Verbalize your thought process and treat each follow-up as a new problem: restate assumptions, consider alternative data structures, and analyze time/space complexity before coding. This shows adaptability and structured thinking, which Google values.
Ask questions to understand input/output, constraints, and edge cases (e.g., empty tree, skewed tree). Confirm assumptions before proceeding.
Choose an appropriate traversal (DFS/BFS) and data structures. Write clean code, explaining your reasoning and analyzing complexity.
Determine what changed: new constraints, different output, or additional operations. Discuss how this impacts your previous solution.
Modify your approach step by step, reusing as much as possible. Consider alternative algorithms or data structures if needed, and analyze trade-offs.
Walk through examples, including edge cases, for each variation. Verify correctness and complexity, and be ready to optimize further.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.