Kanban Board: Build a Drag-and-Drop Task Management UI with Column State Management
Interview Experience
Round 1 Frontend
Problem
Build a Kanban board component in React with three columns:
To Do,
In Progress,
Done. Each task card shows a title and priority badge. Users can drag cards between columns; the drop target column highlights on hover.
tsx
type Task = { id: string; title: string; priority: "low" | "medium" | "high" };
type Column = { id: string; label: string; tasks: Task[] };
function KanbanBoard({ initialColumns }: { initialColumns: Column[] }) {
// implement
}
Requirements:
- Use only the HTML5 Drag and Drop API (no external library).
- State is managed locally; no backend calls needed.
- Dragging a card out of a column removes it there and inserts it at the drop position in the target.
Follow-ups
1. How would you persist board state across page refreshes without a backend?
2. Two users edit the board simultaneously. What conflict resolution strategy would you use?
3. The board may grow to 500 tasks per column. How do you avoid rendering performance issues?
4. Add keyboard accessibility so cards can be moved without a mouse.
Full Details
Round 1 Frontend
Problem
Build a Kanban board component in React with three columns:
To Do,
In Progress,
Done. Each task card shows a title and priority badge. Users can drag cards between columns; the drop target column highlights on hover.
tsx
type Task = { id: string; title: string; priority: "low" | "medium" | "high" };
type Column = { id: string; label: string; tasks: Task[] };
function KanbanBoard({ initialColumns }: { initialColumns: Column[] }) {
// implement
}
Requirements:
- Use only the HTML5 Drag and Drop API (no external library).
- State is managed locally; no backend calls needed.
- Dragging a card out of a column removes it there and inserts it at the drop position in the target.
Follow-ups
1. How would you persist board state across page refreshes without a backend?
2. Two users edit the board simultaneously. What conflict resolution strategy would you use?
3. The board may grow to 500 tasks per column. How do you avoid rendering performance issues?
4. Add keyboard accessibility so cards can be moved without a mouse.
About Sofi Interview Reports
This question was reported by a candidate who interviewed at Sofi. 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 Sofi are the higher-signal extractions to take from this report.
For broader preparation context, the Sofi 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 Sofi reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Sofi 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 Sofi 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.