Google

PART 1: ALGORITHMIC PROBLEM - AHO-CORASICK & DYNAMIC AUTOMATONS BASE PROBLEM You are building a real-time content moderation engine. You are given a massive stream of text and a dictionary of $K$ forbidden keywords. Task: Design an algorithm to detect all occurrences of any forbidden keyword in the text stream. Running the KMP (Knuth-Morris-Pratt) algorithm individually for every keyword takes $O(K \cdot N)$ time, which is too slow. How do you construct an Aho-Corasick Automaton? Specifically, explain the mathematical intuition behind building the Prefix Trie and the Breadth-First Search (BFS) used to construct the "Failure Links" (suffix links). What is the exact time complexity of building the automaton and processing the text? FOLLOW-UP 1 The text structure changes from a linear stream to a hierarchical conversation thread represented as a Tree with $N$ nodes, where each node contains a single character. You must find the total number of keyword occurrences along all unique paths from the root to the leaves. A naive approach would extract every path into a string and run Aho-Corasick, taking $O(N^2)$ time. How do you pass the Aho-Corasick state pointer directly down a Depth-First Search (DFS) traversal of the tree, backtracking the automaton state when returning to a parent, to solve the entire tree in strictly $O(N)$ time? FOLLOW-UP 2 The moderation engine must now support dynamic dictionary updates. Interleaved with the text processing, moderators can add new forbidden keywords to the dictionary on the fly. Rebuilding the entire Aho-Corasick automaton from scratch on every insertion takes $O(\Sigma |L|)$ time, causing massive latency spikes. How do you implement a "Binary Lifting / Logarithmic Rebuilding" technique—maintaining $\log_2(K)$ separate Aho-Corasick automatons of sizes $2^0, 2^1, 2^2, \dots$—to support online string insertions in amortized $O(|L| \log K)$ time while still answering queries efficiently? ------------------------------------------------ PART 2: SYSTEM DESIGN - SERVERLESS FaaS PLATFORM (AWS LAMBDA) BASE PROBLEM You are designing a distributed Serverless "Function-as-a-Service" (FaaS) platform. Developers deploy snippets of code, and the platform automatically provisions execution environments to run them in response to HTTP API triggers. Design the high-level architecture. Focus heavily on the Control Plane (the API Gateway and Router) and the Data Plane (the worker nodes). How does the system dynamically route incoming requests to warm containers, and what is the distributed mechanism for deciding when to spin up a new container versus queueing the request? FOLLOW-UP 1 A massive "Cold Start" problem emerges. When a burst of 100,000 concurrent requests hits a function that currently has zero running instances, the system must initialize 100,000 isolated environments, pull the user's code, and start the runtimes. Stepping away from low-level OS virtualization, focus on the high-level distributed scheduling. How do you design a highly concurrent Placement Service and a globally distributed Hash Table (or Gossip Protocol) to keep track of available compute capacity across 10,000 physical nodes in real-time, ensuring the control plane can dispatch these 100,000 startup commands in milliseconds without overwhelming any single physical machine? FOLLOW-UP 2 Developers now want to chain multiple serverless functions together into complex, long-running workflows (e.g., Step 1: Process Image $\rightarrow$ Step 2: Extract Text $\rightarrow$ Step 3: Save to DB), including conditional branching and retry logic (similar to AWS Step Functions). Executing these state machines by keeping a master function running and waiting completely wastes billing cycles. How do you design a distributed State Machine Orchestrator using the Event Sourcing pattern? Detail how events are durably appended to a partitioned log (like Kafka or DynamoDB Streams) to asynchronously trigger the next function in the DAG, allowing the orchestration engine to track millions of concurrent workflows completely statelessly and lock-free. ------------------------------------------------ PART 3: AI / LLM DISCUSSION QUESTIONS In high-throughput LLM serving, what is "Continuous Batching" (or iteration-level scheduling, as introduced by Orca)? How does it fundamentally solve the GPU under-utilization problem caused by standard static batching when sequences in the same batch finish generating at vastly different times? Explain the mechanical differences between standard Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI (RLAIF). How does Constitutional AI use a critique-and-revise pipeline driven by a foundational model and a set of human-written principles to generate synthetic preference data, entirely bypassing the bottleneck of human annotators? In modern large-scale optimization for training massive models (100B+ parameters), what is the architectural motivation behind replacing the standard AdamW optimizer with the Lion (EvoLved Sign Momentum) optimizer? Specifically, how does Lion's reliance on the mathematical sign operation rather than variance tracking reduce the optimizer's VRAM footprint by up to 50%? How do native "Any-to-Any" multimodal architectures (like Chameleon) tokenize images and text into a single unified discrete vocabulary? Why does this approach allow the model to interleave text and images seamlessly in a single output stream, and what are the specific algorithmic challenges of applying standard autoregressive next-token prediction to 2D visual patches?