Discord Interview Questions (May 2026)
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Bad Word Checking: Detect and Censor Profanity in User Text Using Exact and Fuzzy Matching
Chat Server: Design a Real-Time Messaging System with Rooms, Message History, and User Presence
Leader-Follower System: Implement a Primary/Replica Coordination Module
Message Reaction Feature: Design a Emoji Reaction System for a Messaging App
Discord SWE Phone - Organize Log Events
Spreadsheet: Build an In-Memory Spreadsheet with Cell Dependency Resolution
Bad Word Checking: Detect and Censor Profanity in User Text Using Exact and Fuzzy Matching
Question Details
Problem
Implement a content moderation function that detects bad words in user-submitted text. Matching must handle:
1. Exact match (case-insensitive).
2. Leet-speak substitutions: 3->e, @->a, 0->o, 1->i.
3. Repeated characters: haaaate matches hate.
Return the sanitized string with each bad word replaced by ***.
python
def censor(text: str, blocklist: list[str]) -> str:
pass
Example:
blocklist = ["hate", "spam"]
text = "I h@t3 sp@@m and haaaate it!"
**output** -> "I *** *** and *** it!"
Follow-ups
1. Leet-speak normalization should happen before deduplication or after? Why does the order matter?
2. How would you build a Trie over the normalized blocklist for fast multi-pattern matching (Aho-Corasick)?
3. False positives: "assassination" contains "ass". How do you reduce them without a huge allowlist?
4. The system processes 100,000 messages per second. What architecture would you use for real-time filtering?
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Discord Interview Process Overview
The Discord interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Discord runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Discord coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Discord Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Discord updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Discord reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Discord's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Discord Interview Mistakes
Reports tagged "no hire" at Discord consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.