Linear scan with a stack felt natural here.
Start by clarifying the requirements: what constitutes a valid token, how to handle edge cases like self-closing tags and attributes, and the expected input/output formats. Then design a tokenizer that uses a state machine or regex to scan the string, and also accepts a pre-tokenized list by simply validating and classifying each token. Discuss trade-offs between simplicity and robustness, and consider performance for large inputs.
Pro tip: Mention that you would use a streaming approach for large XML strings to avoid loading everything into memory, and that you'd write unit tests for edge cases like nested tags and malformed input.
Ask about the expected input format, token types, handling of attributes, self-closing tags, comments, CDATA, and malformed XML. Confirm whether the pre-tokenized list contains raw strings or already classified tokens.
Choose between a state machine, regex, or parser-based approach. For the string input, scan character by character or use regex to identify tags and text. For the pre-tokenized list, iterate and classify each token based on its content.
Define rules: tokens starting with '</' are close tags, tokens starting with '<' and ending with '/>' are self-closing (treat as open+close or separate), tokens starting with '<' are open tags, and everything else is raw text. Handle attributes within tags.
Address nested tags, escaped characters, comments, CDATA sections, and malformed input. Decide whether to throw errors or skip invalid tokens. Ensure the pre-tokenized list input is validated for consistency.
Compare performance of regex vs state machine, memory usage for large inputs, and extensibility for future token types. Mention testing strategy and potential use of existing libraries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the token types and XML grammar, then propose a stack-based single-pass parser that processes tokens sequentially. Emphasize that the stack depth equals the tree height, giving O(h) space, and that each token is processed once, giving O(n) time. Discuss how to detect and raise exceptions for mismatched tags, unclosed tags, and stray text.
Pro tip: Mention that using an explicit stack (instead of recursion) avoids stack overflow for deeply nested XML and makes the O(h) space bound clear. Also, discuss how to handle self-closing tags and attributes to show thoroughness.
Ask about the token list format (e.g., start tag, end tag, text, self-closing tag) and any constraints. Confirm that the parser should validate well-formedness and raise exceptions on errors.
Use a stack to track open tags. Iterate through tokens: push on start tag, pop and match on end tag, and validate text placement. Ensure O(n) time by single pass and O(h) space by stack size.
Define specific exceptions for mismatched tags (end tag doesn't match top of stack), unclosed tags (stack not empty at end), and stray text (text outside root or between tags where not allowed).
Explain why time is O(n) (each token processed once) and space is O(h) (stack depth equals nesting depth). Discuss alternative approaches like recursive descent and their trade-offs.
Walk through examples: valid XML, mismatched tags, unclosed tags, stray text, self-closing tags, and deeply nested structures to demonstrate correctness and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the internal representation of the XMLParser (e.g., tree of nodes with attributes and text). Then design a recursive traversal that serializes each node, handling elements, attributes, text, and special cases like self-closing tags and escaping. Finally, discuss how to ensure the output matches the original XML as closely as possible, including whitespace and ordering.
Pro tip: Mention that you would write unit tests comparing the reconstructed XML to the original, and consider edge cases like empty elements, special characters, and namespaces. This shows attention to correctness and robustness.
Ask or state assumptions about how the XML is stored (e.g., node objects with tag, attributes, children, text). This ensures the serialization logic aligns with the data structure.
Outline a recursive function that processes each node: open tag with attributes, then children/text, then close tag. Handle self-closing tags when there are no children or text.
Discuss escaping special characters in text and attribute values (e.g., &, <, >, quotes). Also consider namespaces, CDATA sections, and comments if supported.
Decide whether to preserve original whitespace and attribute order. If the internal representation doesn't store them, mention that the output may differ and how to mitigate (e.g., canonicalization).
Propose unit tests that parse XML, serialize it, and compare to the original (or re-parse to check equivalence). Include edge cases like empty elements, nested structures, and special characters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data structure (e.g., tree with parent-child relationships) and the path format (e.g., array of indices or string path). Then outline a step-by-step algorithm: traverse the tree to the target node, validate the index, create the new node with optional text, and insert it at the specified position. Discuss edge cases and complexity.
Pro tip: Mention that you would handle edge cases like invalid paths, out-of-bounds indices, and empty trees gracefully, and consider whether the tree is mutable or immutable. Also, discuss the time complexity (O(depth) for traversal) and potential optimizations like caching or using a sentinel node.
Ask questions to understand the tree representation, path format, and expected behavior for edge cases. Confirm whether the tree is mutable and if the method should return anything.
Plan how to navigate from the root to the target node using the given path. Consider iterative vs recursive approaches and how to handle invalid paths.
Check that the index is within bounds (0 to number of children). Create the new node with the optional text content.
Insert the new node at the specified index, updating the children list. Address edge cases such as empty tree, root insertion, and index at the end.
Discuss time and space complexity, and walk through test cases including normal, boundary, and error scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you have the path traversal logic from add_element.
Clarify the tree structure and path format, then recursively traverse the tree following the path components. Handle deletion by considering cases: leaf node, node with one child, and node with two children (if binary tree), or simply remove from children list (if n-ary).
Pro tip: Discuss edge cases like root deletion, invalid path, and maintaining tree properties; also mention time complexity O(d) where d is depth, and space complexity O(d) for recursion.
Ask about tree type (binary, n-ary), path representation (e.g., list of indices or values), and expected behavior for invalid paths or root deletion.
Use recursion to traverse the tree following the path. At each step, move to the appropriate child based on the path component.
Once the target node is found, handle deletion based on its children: if leaf, remove it; if one child, replace with child; if two children (binary tree), find inorder successor/predecessor and replace.
Consider root deletion, empty tree, path not found, and updating parent pointers if applicable. Return the new root if necessary.
State time complexity O(d) where d is depth of target node, and space complexity O(d) due to recursion stack.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They specifically said no recursion, which I appreciated as a constraint because it actually tests something.
Start by clarifying the problem: iterative preorder DFS using an explicit stack. Then walk through the algorithm step-by-step, emphasizing the LIFO property and the order of pushing children (right before left) to ensure correct preorder. Finally, discuss time and space complexity and potential edge cases.
Pro tip: Mention that you can optimize space by pushing only non-null children, and note that the stack size is O(h) for a balanced tree but O(n) worst-case. This shows awareness of practical performance beyond the basics.
Confirm that the tree is binary, nodes have left/right children, and preorder means root, left subtree, right subtree. Ask if the tree can be empty or have cycles (though typically it's a tree).
Explain that you'll use a stack initialized with the root. While the stack is not empty, pop a node, process it, then push its right child followed by its left child (so left is processed next).
Trace the algorithm on a small tree (e.g., 1 with left 2 and right 3) to demonstrate the stack operations and output order.
State that time complexity is O(n) since each node is pushed/popped once, and space is O(h) for the stack in the best case, O(n) worst-case. Mention handling empty tree and skewed trees.
Optionally, mention that you can avoid pushing null children, or that the same pattern works for n-ary trees by pushing children in reverse order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.