← Capital One Interview Insights
Took me a minute to parse the comparison logic since you're comparing against the mirrored index in s2, not the same index.
First, clarify the problem by restating it and confirming edge cases like empty strings or case sensitivity. Then, walk through a concrete example to demonstrate the logic, and finally write clean code with clear variable names, explaining each step as you go.
Pro tip: Mention that you would verify the solution with a few test cases, including edge cases, and discuss the time and space complexity upfront to show you think about efficiency.
Restate the problem in your own words and ask clarifying questions about input constraints, character types, and expected output format.
Choose a simple example (e.g., s1='abc', s2='xyz') and manually compute the output to verify your understanding and the algorithm.
Outline the steps: iterate through indices, append s1[i], compare with s2[n-1-i], and append accordingly. Consider using a StringBuilder for efficiency.
Write the code in your preferred language, using clear variable names and comments. Handle edge cases like empty strings.
Run through test cases, including edge cases, and state the time and space complexity (O(n) time, O(n) space).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as a shortest path search on a state graph where each state is (cell, health). Use Dijkstra's algorithm or BFS with a priority queue, treating health as a resource that can be replenished by healing cells and depleted by monster cells. Track the best health for each cell to prune suboptimal paths, and return the shortest distance or reconstruct the path.
Pro tip: Clarify upfront whether health can be regained and whether revisiting cells with different health is allowed; this determines if the state space is (cell, health) or just cell. Also, mention that if health is bounded, you can use a 3D visited array to avoid exponential blowup.
Ask about grid size, health bounds, movement rules (4-directional?), and whether healing/monster effects are fixed or variable. Confirm if revisiting cells with different health is allowed.
Represent each state as (row, col, current_health). Edges connect adjacent cells, with health updated based on the target cell's type. Ensure health never drops to zero or below.
Use Dijkstra's algorithm (or BFS if all edges have equal weight) to find the shortest path. Prioritize states by distance, and track the maximum health achievable for each cell to prune dominated states.
Maintain a visited array or hash map storing the best health seen for each cell. Skip states that are dominated (same cell, lower or equal health and greater or equal distance). Reconstruct the path if needed.
Discuss time and space complexity in terms of grid size and health range. Handle edge cases: start health too low, unreachable goal, healing cells that exceed max health, and cycles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Details were vague in my notes so I can't say exactly what the constraint was.
Start by clarifying the problem and identifying the constraint that makes sliding window applicable, such as a fixed window size or a monotonic condition. Then explain how to maintain a window that always satisfies the constraint, expanding and shrinking it while tracking the required result. Finally, walk through the algorithm with a small example and state the time and space complexity.
Pro tip: Explicitly discuss when the window should shrink and how you maintain the constraint, because interviewers at Capital One look for clean, bug-free implementations and clear reasoning about edge cases like empty input or all elements satisfying the condition.
Restate the problem in your own words and confirm the exact constraint the window must satisfy, including edge cases like empty input or negative numbers.
Decide whether a fixed-size or variable-size window is needed, and identify any auxiliary data structures (e.g., hash map, deque) to track window state efficiently.
Define the conditions for expanding the right pointer and shrinking the left pointer, ensuring the window always satisfies the constraint after each adjustment.
Specify what to record (e.g., max length, min length, start index) and when to update it as the window changes.
State the time and space complexity, then walk through a small example and edge cases to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.