First, clarify the problem details and constraints, then choose the appropriate traversal (BFS or DFS) based on the graph type and problem requirements. Implement the traversal with careful attention to visited tracking and edge cases, and analyze time and space complexity.
Pro tip: Always discuss trade-offs between BFS and DFS, and mention how you would handle large graphs or disconnected components. This shows depth and practical awareness.
Ask questions to understand the graph representation (adjacency list/matrix), directed/undirected, connected/disconnected, and the specific goal (e.g., shortest path, cycle detection).
Decide between BFS and DFS based on the problem: BFS for shortest path in unweighted graphs, DFS for topological sort or cycle detection. Explain your choice.
Describe the steps: initialize data structures (queue/stack, visited set), iterate through nodes, and process neighbors. Mention handling of disconnected components.
Write clean code, then walk through a small example and edge cases (empty graph, single node, cycles). Discuss time and space complexity.
Consider optimizations like early termination, bidirectional search, or iterative vs recursive DFS. Discuss trade-offs and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: identify the array structure, partition definition, and the objective (e.g., find a partition point, search for a target). Then, design a binary search algorithm that leverages the partition property, and rigorously analyze its time complexity to prove it is better than O(n).
Pro tip: Always state the assumptions about the input (e.g., sorted partitions, monotonicity) before diving into the solution; this shows you think about edge cases and problem constraints.
Ask questions to understand the array structure, how partitions are defined, and what 'optimal solution' means (e.g., minimize comparisons, find a specific element).
Determine if the problem has a monotonic property or can be reduced to searching in a sorted or partially sorted structure, enabling binary search.
Outline the binary search steps: define search space, mid calculation, condition to move left/right, and termination. Handle edge cases like empty partitions or duplicates.
Show that each step halves the search space, leading to O(log n) or O(log m) where m is number of partitions, and argue why this is better than O(n).
Mention any assumptions (e.g., sorted partitions), potential pitfalls (e.g., non-uniform partition sizes), and compare with linear scan.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Phone screen had a behavioral component alongside the DSA question.