The disguise was the whole challenge here.
First, restate the problem in your own words and identify the underlying structure by looking for entities and relationships that can be modeled as nodes and edges. Then, choose the appropriate graph algorithm (e.g., BFS, DFS, Dijkstra, topological sort) based on the problem's constraints and optimize for time/space complexity.
Pro tip: Always discuss trade-offs between different graph representations (adjacency list vs. matrix) and algorithm choices, and mention how you would handle edge cases like disconnected graphs or cycles.
Clarify the problem statement, inputs, outputs, and constraints. Ask questions if needed to ensure you fully grasp the requirements.
Determine what the nodes and edges represent. Look for relationships, dependencies, or connections that can be modeled as a graph.
Based on the problem type (e.g., shortest path, connectivity, cycle detection), choose an algorithm like BFS, DFS, Dijkstra, or Union-Find, and justify your choice.
Evaluate the time and space complexity of your solution. Consider optimizations such as using adjacency lists for sparse graphs or early termination.
Walk through examples, including edge cases like empty graphs, disconnected components, or cycles, to validate your solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a specific past experience where you navigated ambiguity or adapted to change. Structure your answer with the STAR method, emphasizing the actions you took and the results, and explicitly connect it to Google's values like 'Googleyness' and comfort with ambiguity.
Pro tip: Show that you not only adapted but also helped others navigate ambiguity, demonstrating leadership and collaboration. Quantify the impact of your actions to make your story more compelling.
Briefly describe the situation, highlighting the ambiguous or changing circumstances. Explain why it was challenging and what was at stake.
Explain how you analyzed the situation, gathered information, and made decisions despite uncertainty. Emphasize your thought process and any frameworks you used.
Detail the specific steps you took to adapt, including how you communicated with others, adjusted plans, and kept the team aligned.
Quantify the outcome if possible, and reflect on what you learned. Connect the experience to how you would handle similar situations at Google.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The wrong problem statement thing was awkward.
First, clarify the problem statement by restating it and asking targeted questions to resolve ambiguities. Then, outline your approach, discuss trade-offs, and implement a clean solution. Finally, adapt to the follow-up variant by identifying how the problem changes and modifying your solution accordingly.
Pro tip: When the interviewer pastes an incorrect problem, don't just silently fix it—politely point out the inconsistency and ask for clarification. This shows attention to detail and proactive communication, which are highly valued at Google.
Restate the problem in your own words and ask specific questions to resolve any ambiguities or errors in the initial statement.
Before coding, explain your high-level approach, including data structures and algorithms, and discuss time/space trade-offs.
Write clean, modular code and walk through test cases, including edge cases, to verify correctness.
State the time and space complexity of your solution and consider if any optimizations are possible.
Listen carefully to the variant, identify how it differs, and adapt your solution, discussing necessary changes and their implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the graph problem, constraints, and expected output. Then, discuss and implement an initial solution (e.g., BFS/DFS) with complexity analysis. For the follow-up, adapt your approach to the new requirement, possibly using a different algorithm or data structure, and compare trade-offs.
Pro tip: Demonstrate proactive communication: before coding, restate the problem and confirm assumptions with the interviewer. After the first implementation, explicitly discuss potential optimizations and edge cases before moving to the follow-up.
Ask questions to clarify the graph representation (adjacency list/matrix), constraints (size, edge weights), and expected output. Confirm any assumptions.
Choose an appropriate traversal (BFS/DFS) or algorithm (Dijkstra, topological sort) based on requirements. Outline the approach and analyze time/space complexity.
Write clean, modular code for the initial solution. Walk through a small example to verify correctness and handle edge cases.
Listen carefully to the extension. Identify how it changes the problem (e.g., dynamic updates, different constraints) and propose a modified or alternative approach.
Discuss trade-offs between the two implementations. Suggest optimizations or alternative data structures if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.