## Problem You are given a list of `(node_id, parent_id)` pairs representing a forest (collection of trees). A root node is one that does not appear as a child of any other node. Return all root node IDs in sorted order. ```python def find_root_ids(edges: list[tuple[int, int]]) -> list[int]: """edges: list of (node, parent). Return sorted list of roots.""" pass ``` ``` Input: edges = [(2,1), (3,1), (4,2), (5,3), (6,0)] Output: [1, 0] # Node 1 has no parent listed -> root # Node 0 has no parent listed -> root # Sorted: [0, 1] Input: edges = [(2,1), (3,2)] Output: [1] ``` ## Follow-ups 1. What if the input contains a cycle? How would you detect and report it? 2. How would you reconstruct the full tree structure (children mapping) from this edge list? 3. If `node_id` values are strings instead of integers, how does your solution change? 4. How would you find all leaf nodes in O(n) using the same edge list?

InterviewDB Experience

Root IDs: Find All Root Nodes in a Forest Given Parent Pointers

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Interview Experience

Problem You are given a list of (node_id, parent_id) pairs representing a forest (collection of trees). A root node is one that does not appear as a child of any other node. Return all root node IDs in sorted order. Follow-ups What if the input contains a cycle? How would you detect and report it? How would you reconstruct the full tree structure (children mapping) from this edge list? If node_id values are strings instead of integers, how does your solution change? How would you find all leaf n…

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About This Question

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

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

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Coding Phone Onsite Strings

About Ziphq Interview Reports

This question was reported by a candidate who interviewed at Ziphq. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Ziphq are the higher-signal extractions to take from this report.

For broader preparation context, the Ziphq interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

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 Ziphq reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Ziphq Round

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 Ziphq 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.