I solved it but made a rookie mistake with a char vector.
Start by clarifying the exact string processing task and constraints (input size, character set, memory limits). Then outline an algorithm that minimizes extra space, such as in-place manipulation or streaming, and discuss time/space trade-offs. Finally, write clean, readable code with comments and test edge cases.
Pro tip: Mention that you would consider Unicode and multi-byte characters, and propose a streaming approach for inputs that don't fit in memory, showing awareness of real-world scalability.
Ask about input size, character encoding, memory limits, and expected output format to ensure you solve the right problem.
Select an approach that minimizes time and space complexity, such as two-pointer techniques, in-place operations, or streaming with a fixed-size buffer.
Implement the solution with clear variable names, helper functions if needed, and comments explaining memory optimizations.
State the time and space complexity, and discuss alternatives (e.g., using more memory for speed) and why your choice is optimal.
Walk through examples including empty strings, large inputs, and special characters to verify correctness and robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went down a rabbit hole with nested pairs and maps of vectors before the interviewer nudged me toward using a struct.
Model the airports and flights as a directed graph where nodes are airports and edges are flights with time constraints. Then use DFS or BFS to find valid routes, ensuring that each connection respects the arrival and departure times. Clarify whether you need all possible routes, the shortest route, or just connectivity, and discuss time/space complexity.
Pro tip: Always clarify the problem constraints first—ask about the number of airports, flights, time format, and whether routes can have cycles or multiple legs. This shows you think about edge cases and scalability before coding.
Ask questions to understand the input format, output expectations, and constraints (e.g., directed vs undirected, time zones, layover times).
Represent airports as nodes and flights as directed edges, storing departure and arrival times on edges or nodes.
Select DFS for finding all paths or BFS/Dijkstra for shortest paths, incorporating time validity checks during traversal.
During traversal, only follow edges where the departure time is after the current arrival time (plus any layover).
Discuss time and space complexity, and suggest optimizations like sorting flights by time or using priority queues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use binary search on rows and columns to find the missing row and column independently, leveraging the countRook API to check if a range contains all expected rooks. Each binary search takes O(log n) calls, totaling O(log n) calls, which is optimal.
Pro tip: Clarify the API's behavior and coordinate system upfront, and mention that the solution is optimal because each call provides at most one bit of information, so Ω(log n) calls are necessary.
Confirm the board size n, the number of rooks (n-1), the API's parameters (inclusive/exclusive boundaries, coordinate system), and that rooks are placed with no two sharing a row or column.
Binary search on rows: for a range of rows [r1, r2], call countRook(r1, 1, r2, n). If the count equals the number of rows in the range, the empty row is outside; otherwise, it's inside. Narrow down until one row remains.
Similarly, binary search on columns: for a range of columns [c1, c2], call countRook(1, c1, n, c2). Compare the count to the number of columns in the range to determine if the empty column is inside.
Each binary search takes ⌈log₂ n⌉ calls, so total calls are 2⌈log₂ n⌉. Argue that this is optimal because each API call returns a count that can distinguish at most two cases (missing inside or outside), giving a lower bound of Ω(log n) calls.
Consider n=1 (no rooks, empty row and column are 1), and discuss if the API can be used differently (e.g., ternary search) but binary search is optimal. Mention that the two searches are independent and can be done in any order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to tell a concise story about a time you navigated ambiguity or a challenge, explicitly linking your actions to Google's values like user focus, innovation, and collaboration. Emphasize your thought process, adaptability, and measurable impact while showing self-awareness and a growth mindset.
Pro tip: Google values 'Googleyness'—demonstrate intellectual humility by acknowledging what you didn't know and how you learned, and show that you prioritize team success over individual glory.
Briefly describe the situation, including the ambiguity or challenge, and why it mattered. Keep it concise to leave time for your actions and results.
Detail the steps you took to navigate the ambiguity: how you gathered information, made decisions, and adapted. Highlight collaboration and user-centric thinking.
Describe any obstacles or changes and how you pivoted. Emphasize learning from feedback and iterating quickly.
Share the outcome with metrics if possible, and reflect on what you learned and how it aligns with Google's values.
Explicitly tie your story to Google's culture and values, such as innovation, user focus, or collaboration, showing you've done your research.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.