Discord Software Engineer Phone Screen Questions

## 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?

## Problem Design a `ChatServer` class that manages chat rooms: - `create_room(room_id)` -- create a new room. - `join_room(user_id, room_id)` -- add user to room. - `leave_room(user_id, room_id)` -- remove user from room. - `send_message(user_id, room_id, text) -> Message` -- broadcast message; return message object with timestamp. - `get_history(room_id, limit=50) -> list[Message]` -- return the `limit` most recent messages. - `online_users(room_id) -> list[str]` -- return currently joined users. ```python from dataclasses import dataclass from datetime import datetime @dataclass class Message: id: int user_id: str room_id: str text: str timestamp: datetime class ChatServer: ... ``` **Example:** ``` server.create_room("general") server.join_room("alice", "general") server.send_message("alice", "general", "hello") server.get_history("general") -> [Message(...)] ``` ## Follow-ups 1. `get_history` must be fast even with millions of messages. What storage structure and index would you use? 2. A user disconnects without calling `leave_room`. How do you detect and clean up stale presence? 3. How would you fan out a message to 10,000 users in the same room with low latency? 4. Add read receipts: track which users have seen each message up to which message id.

## Problem Parse and organize log events by timestamp or category, possibly merging overlapping or deduplicating events. ## Likely LeetCode equivalent No direct match with high confidence. ## Tags sorting, arrays, hash_table