Google

PART 1: ALGORITHMIC PROBLEM - AUTOCOMPLETE SYSTEM BASE PROBLEM You are building the search bar for an e-commerce website. You are given a large dictionary of 'N' lowercase English words. Task: Design a data structure and implement a search function that takes a string 'prefix' and returns a list of ALL words in the dictionary that start with that exact prefix. What is the most efficient data structure for this, and what is the time complexity of building it and searching it? FOLLOW-UP 1 The dictionary is massive, and returning every single word that matches a prefix (like "a") is too slow and crashes the UI. Each word now has an associated 'historical search frequency' integer. Task: Modify your data structure so that when a 'prefix' is searched, it ONLY returns the top 'K' most frequently searched words starting with that prefix. How can you optimize the data structure so that the search operation remains extremely fast (ideally O(length of prefix + K)) without sorting the results every single time a user types a letter? FOLLOW-UP 2 The e-commerce site is now global. The dictionary and historical frequencies are updated millions of times per second, and the data is too large to fit in the RAM of a single server. Task: How do you partition (shard) this data structure across multiple servers? Discuss the pros and cons of sharding alphabetically (e.g., Server 1 gets A-C, Server 2 gets D-F) versus hashing the prefixes. How do you handle "hotspots" where a specific prefix goes viral and overwhelms a single server? PART 2: AI DISCUSSION & TERMINOLOGY Practical Application & Security: What is a "Prompt Injection" attack? If you are building an AI assistant for a business, how do you prevent malicious users from tricking the AI into ignoring its safety rules or revealing hidden system instructions? General Knowledge: In modern AI architectures (like Transformers), what does the "Attention Mechanism" do in simple terms? Why was it a breakthrough compared to older models that read text strictly left-to-right? Basic Terminologies (Briefly explain the following concepts): Multimodal AI: An AI model that is not limited to just text. It can simultaneously understand, process, and generate multiple types of data, such as images, audio, video, and text within the same interaction. Chain-of-Thought (CoT) Prompting: A technique where you instruct the AI to "think step-by-step" before giving the final answer. Forcing the model to output its intermediate reasoning drastically improves its accuracy on complex math or logic problems. Quantization: A technique used to compress a massive AI model so it can run on smaller, cheaper hardware (like a local laptop or smartphone). It works by reducing the mathematical precision of the model's internal parameters (e.g., from 32-bit decimals to 8-bit integers). Semantic Search: Unlike traditional keyword search (which looks for exact word matches), semantic search uses AI embeddings to understand the meaning or intent behind a query. For example, searching for "fast car" will successfully return documents about "sports automobiles" even if the exact words aren't there.