This felt like two questions jammed into one.
Start by clarifying requirements and edge cases, then propose a tree-based data structure where each node represents a path component and stores a value if it's a leaf. Implement operations with careful handling of parent existence and leaf constraints, and analyze time complexity based on path depth and component length.
Pro tip: Discuss trade-offs between using a tree versus a hash map with path strings, and mention how to handle concurrent access if needed. Also, proactively bring up testing strategies for edge cases like root operations and deep paths.
Ask questions to confirm assumptions: Are keys case-sensitive? Can values be any type? What should happen if create is called on an existing path? Should delete be recursive? Clarify that root always exists and cannot be deleted.
Propose a tree where each node has a map of child name to node, and an optional value. Alternatively, consider a hash map from full path to value, but explain why tree is better for hierarchical operations and constraints.
Detail each operation: create(path) checks parent exists and path doesn't exist, then adds node; set(path, value) requires path exists and is leaf; get(path) returns value if leaf; delete(path) removes leaf node if it has no children and is not root.
For tree approach, each operation traverses the path components, so O(k) where k is number of components, assuming O(1) child lookup. Discuss space complexity O(total nodes). Compare with hash map approach O(1) average but O(L) for string hashing where L is path length.
Cover edge cases: root operations, creating under non-existent parent, deleting non-leaf, setting on non-leaf. Mention possible extensions: atomic operations, concurrency, persistence, or efficient listing of children.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.