## Problem Implement a simplified in-memory DOM tree. Each node has a `tag`, optional `id`, a list of `classes`, and child nodes. ```python class DOMNode: def __init__(self, tag: str, id: str = "", classes: list[str] = []): ... def append_child(self, child: 'DOMNode') -> None: ... def query_selector(self, selector: str) -> 'DOMNode | None': # supports: "tag", "#id", ".class", "tag.class" def query_selector_all(self, selector: str) -> list['DOMNode']: def remove_child(self, child: 'DOMNode') -> bool: ``` ## Example ```javascript // equivalent structure: // // ... // ... // root.query_selector(".intro") // -> first root.query_selector_all(".intro") // -> [ , ] root.query_selector("#root") // -> root div ``` ## Follow-ups 1. How would you support a descendant combinator, e.g., `"div p"` (all ` ` inside ` `)? 2. Implement `innerHTML` that serializes the subtree back to an HTML string. 3. What traversal strategy does `query_selector` use? Why BFS vs. DFS matters here. 4. How do you handle `query_selector` on a very deep tree (10K levels) without stack overflow?

InterviewDB Experience · Los Angeles

DOM Tree Manipulation - Build and Query a Simplified HTML DOM

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Problem Implement a simplified in-memory DOM tree. Each node has a tag, optional id, a list of classes, and child nodes. Example Follow-ups How would you support a descendant combinator, e.g., "div p" (all <p> inside <div>)? Implement innerHTML that serializes the subtree back to an HTML string. What traversal strategy does query_selector use? Why BFS vs. DFS matters here. How do you handle query_selector on a very deep tree (10K levels) without stack overflow?

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This is a candidate experience report from a affirm interview during the phone round.

It covers the following topics: Strings, Phone, Graph, Coding, Onsite, Stack .

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How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Affirm reports consistently are the ones worth investing in; one-off niche problems are not.

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Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Affirm reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.