Microsoft

PART 1: ALGORITHMIC PROBLEM - SEARCH AUTOCOMPLETE SYSTEM BASE PROBLEM You are designing the in-memory data structure for a search engine's autocomplete feature. Task: Design a `AutocompleteSystem` class that supports the following: Initialization with a list of historical search sentences and their corresponding frequencies. `input(char c)`: Takes the next character of a user's search. It must return the top 3 historical sentences that start with the current prefix formed so far, sorted by frequency (and then lexicographically for ties). If the character is a special end-symbol (like `#`), the current sentence is saved/updated in the system. What data structure allows you to store these strings and quickly retrieve the top matches based on prefix? FOLLOW-UP 1 A standard Trie requires a Depth-First Search (DFS) from the prefix node to find all possible sentences, which is too slow if the prefix has thousands of suffixes. How do you optimize the Trie nodes (trading space for time) so that `input(char c)` returns the top 3 results in O(1) time relative to the number of stored sentences? FOLLOW-UP 2 User search trends change rapidly throughout the day. When a user finishes a new search, its frequency increases, potentially altering the top 3 results for all its prefixes. If every node caches its top 3 results, how do you dynamically update these cached lists efficiently without rebuilding the entire Trie from scratch? - PART 2: SYSTEM DESIGN - DISTRIBUTED TYPEAHEAD SUGGESTIONS BASE PROBLEM Your single-server autocomplete data structure must now scale to handle Google-level traffic (billions of searches per day with strict sub-50ms latency). Design the distributed backend architecture to aggregate search frequencies and serve typeahead suggestions globally. FOLLOW-UP 1 Ingesting every single keystroke into your database to calculate frequencies will immediately overwhelm your storage layer. How do you design the data gathering service to sample, batch, and aggregate real-time streams (e.g., using a Count-Min Sketch or sliding window logs) before periodically updating the distributed Trie datastore? FOLLOW-UP 2 The Trie datastore is now hundreds of gigabytes and must be partitioned across a cluster of read-replicas. If you partition by the first letter of the prefix (e.g., Node 1 handles 'a' to 'm'), prefixes like 'a' or 's' will receive disproportionately massive traffic, creating a hot partition. How do you redesign your sharding strategy and caching layer (using CDNs or browser-level caching) to balance the load evenly across the cluster? - PART 3: AI / LLM DISCUSSION QUESTIONS What is the difference between Lexical Search and Semantic Search? Lexical search relies on exact keyword matching (like finding the exact string "database" using inverted indices like Elasticsearch). Semantic search uses AI embeddings to understand intent and context. It converts text into high-dimensional vectors, allowing a search for "data storage" to successfully return results for "database" by calculating the mathematical proximity of their vectors, even if the exact keywords do not overlap. How does Retrieval-Augmented Generation (RAG) address data staleness? Standard LLMs are frozen in time based on their training data cutoff. If you ask an LLM about yesterday's stock prices, it will fail or hallucinate. RAG solves this by intercepting the user's prompt, executing a real-time query against a live database or search API to retrieve the most current factual context, and injecting that context into the prompt before generating the final response. What is the difference between an LLM's Context Window and its Training Data? Training data is the massive corpus of text (often terabytes of internet data) used to initially build the model's neural weights over months of computation; this knowledge is permanent but generalized. The Context Window is the temporary working memory available during a single interaction (e.g., 128,000 tokens). Information provided in the context window is highly prioritized for the current task but is completely forgotten the moment the session ends. How do you prevent Prompt Injection in user-facing AI features? Prompt injection occurs when a user inputs text that tricks the AI into ignoring its original instructions. Mitigation strategies in FAANG environments include privilege separation (treating LLM calls like SQL queries, where user input is strictly parameterized), running a secondary, smaller classifier model to scan inputs for malicious intent before passing them to the main LLM, and enforcing strict output parsing (like requiring valid JSON) to limit the impact of a hijacked response.