Google

PART 1: ALGORITHMIC PROBLEM - MULTI-PATTERN STRING MATCHING BASE PROBLEM You are building a content moderation filter for a chat application. You have a dictionary of K forbidden words. You are given a large text string T of length N. Task: Write an algorithm to find and highlight all occurrences of any forbidden word in the text. How would you use a standard Trie data structure to solve this, and what is the worst-case time complexity if the text contains heavily repeated overlapping characters? FOLLOW-UP 1 The basic Trie approach requires backtracking the search pointer in the text whenever a partial match fails, which degrades performance to O(N * max_word_length). Task: How do you optimize this to run in strict O(N + total_matches) time? Discuss the construction of an Aho-Corasick Automaton. Specifically, explain how you compute the "failure links" (or suffix links) during a Breadth-First Search of the Trie, and how these links prevent the algorithm from ever moving backwards in the text T. FOLLOW-UP 2 The chat application is now operating over a raw TCP socket, meaning the text T is an infinite, continuous stream of incoming characters. You cannot buffer the entire text in memory. Task: How does your Aho-Corasick state machine inherently solve this streaming problem? Furthermore, if the dictionary of forbidden words is dynamically updated (words added or removed) while the stream is running, how do you handle rebuilding the automaton without dropping incoming characters or blocking the main processing thread? PART 2: SYSTEM DESIGN - DISTRIBUTED CRON SCHEDULER BASE PROBLEM You are designing a distributed task scheduler (similar to AWS EventBridge or a distributed Cron). Users can submit a payload and a time expression (e.g., "Execute this API call every Tuesday at 3:00 PM" or "Execute exactly once at Unix timestamp X"). Task: Design the core components of this system. How do you store the scheduled tasks, and what mechanism continuously polls or triggers to execute tasks at the precise time? FOLLOW-UP 1 A single polling server is a single point of failure. You scale to a cluster of scheduler nodes, but now you face a concurrency problem: multiple nodes might poll the database, fetch the same task, and execute it multiple times. Task: How do you guarantee high availability while ensuring "at-least-once" or "exactly-once" execution semantics? Discuss the use of distributed locks (like Redis Redlock or Zookeeper), leader election, and how to safely transition a task's state from "pending" to "executing" to "completed." FOLLOW-UP 2 The system scales to process millions of tasks that trigger at the exact same second. Polling a relational database for "tasks due right now" becomes a massive bottleneck and causes database CPU spikes. Task: How do you optimize the triggering mechanism for massive scale? Discuss the transition from database polling to using in-memory Hierarchical Timing Wheels (Hashed Wheel Timers) partitioned across your cluster. How do you route tasks to the correct partition's wheel, and how do you handle nodes crashing and losing their in-memory wheel state? PART 3: AI / LLM DISCUSSION QUESTIONS What is PagedAttention (as implemented in frameworks like vLLM), and how does it solve the memory fragmentation issues of standard KV Caches during high-throughput LLM serving? In the context of LLM alignment, what is the fundamental mathematical and operational difference between Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO)? You are designing an AI Agent that operates over a long-running session (e.g., a virtual software developer). How do you architect the agent's memory system to differentiate between "working memory" (short-term context) and "episodic memory" (long-term recall), and what vector database indexing strategies support this? What is a "System Prompt Leak," and what infrastructure-level defenses or secondary LLM validation loops can you implement to prevent users from extracting your proprietary system instructions?