Didn't get it to fully run, which still bothers me.
First, clarify the problem requirements and constraints, then design a trie with appropriate node structure and operations. Implement the solution step-by-step, explaining time and space complexity, and consider edge cases and potential optimizations.
Pro tip: Demonstrate deep understanding by discussing trade-offs between trie and other data structures (e.g., hash map) and mentioning real-world applications like autocomplete or spell check. Also, proactively handle edge cases such as empty strings or large inputs.
Ask clarifying questions to understand the exact operations needed (insert, search, delete, prefix search) and constraints (character set, memory limits).
Define the trie node with children (e.g., array or hash map) and a flag for end-of-word. Explain the choice based on character set and memory.
Code the required operations (insert, search, startsWith, etc.) iteratively or recursively, ensuring correctness and efficiency.
State time and space complexity for each operation, typically O(m) for time where m is key length, and O(n*m) for space where n is number of keys.
Walk through examples, test edge cases (empty string, long keys), and discuss possible optimizations like compressed tries or ternary search trees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
These rounds always feel like a vibe check more than anything.
Use the STAR method (Situation, Task, Action, Result) to structure your answers, focusing on your thought process and actions. Emphasize how you navigate ambiguity, collaborate with others, and drive towards a solution while demonstrating Google's values like user focus and teamwork.
Pro tip: Show self-awareness by acknowledging trade-offs and what you learned from the experience, and tailor your examples to Google's culture of innovation and user-centricity.
Clarify the hypothetical situation by asking questions if needed, and identify the key challenges and stakeholders involved.
Describe the steps you would take, emphasizing data-driven decision making, collaboration, and user impact.
If possible, relate it to a past experience using the STAR method to demonstrate your skills in action.
Explain how you would adjust to changing circumstances and resolve disagreements constructively.
Conclude with the outcome, what you learned, and how it aligns with Google's values and the role.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Finished in about 20 minutes, gave complexity, and had time to spare.
Start by clarifying the problem and constraints, then discuss potential approaches including brute force and optimized binary search. Explain the binary search logic step-by-step, handle edge cases, and analyze time and space complexity.
Pro tip: At Google, interviewers value clear communication and the ability to derive the solution from first principles. Walk through your thought process, even if you get stuck, and always test your solution with examples.
Ask clarifying questions to confirm the input, output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then identify inefficiencies and propose a binary search approach. Explain why binary search is applicable (e.g., sorted array or monotonic property).
Outline the binary search steps: initialize low and high pointers, compute mid, compare with target, and adjust pointers. Handle duplicates or boundary conditions as needed.
State the time complexity (O(log n)) and space complexity (O(1) for iterative). Compare with brute force to highlight improvement.
Walk through the algorithm with sample inputs, including edge cases like empty array, single element, target not present, and duplicates. Write pseudocode or actual code if required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came out of nowhere since I'd already solved the first one.
First, clarify the problem constraints and edge cases, then choose the appropriate traversal algorithm (BFS, DFS, or topological sort) based on the graph/matrix properties. Implement the solution with clean code, analyze time and space complexity, and test with examples to ensure correctness.
Pro tip: Since this is a follow-up, the interviewer expects you to handle it efficiently; demonstrate strong communication by thinking aloud and proactively discussing trade-offs between different traversal methods.
Ask questions to understand the input format, output requirements, constraints, and edge cases (e.g., disconnected graphs, cycles, empty inputs).
Decide between BFS, DFS, or topological sort based on whether you need shortest paths, cycle detection, or ordering. Consider iterative vs recursive implementations.
Write clean, modular code. Use appropriate data structures (queues, stacks, visited sets) and optimize for time and space complexity.
Walk through test cases, including edge cases, and verify the solution. Discuss potential pitfalls and how to handle them.
Clearly state the time and space complexity, and explain how the chosen approach affects performance.
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 the exact modification to the shortest-path algorithm. Then, break down the problem into smaller subproblems, discuss potential approaches (e.g., Dijkstra with modifications, dynamic programming), and analyze their time/space complexity. Finally, code a clean solution and test with edge cases.
Pro tip: Demonstrate strong communication by thinking aloud and explaining your reasoning at each step. Google values how you approach problems, not just the final answer, so show your thought process and consider trade-offs between different solutions.
Ask questions to fully understand the problem: What is the exact modification? What are the constraints on graph size, edge weights, etc.? Are there any special cases?
Brainstorm possible algorithms (e.g., modified Dijkstra, Bellman-Ford, A*, dynamic programming) and discuss their applicability and trade-offs.
Select the most efficient approach based on constraints, and explain why it works and its time/space complexity.
Write clean, modular code, explaining each part as you go. Handle edge cases and test with examples.
Review the solution for correctness and efficiency. Discuss potential optimizations or alternative approaches if time permits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.