Medium-level software architecture interview questions covering distributed systems, event-driven architecture, and advanced patterns. Q1: Explain Event-Driven Architecture and its benefits. Answer: Definition: Architecture where components communicate through events (state changes) rather than direct calls. Core …

A curated collection of the most common algorithm interview problems with optimal Go solutions. Array Problems 1. Two Sum Problem: Find two numbers that add up to target. 1func TwoSum(nums []int, target int) []int { 2 seen := make(map[int]int) 3 4 for i, num := range nums { 5 complement := target - num 6 if j, exists …

Easy-level algorithm interview questions with detailed approach explanations and solutions. How to Approach Interview Problems Clarify requirements: Ask about edge cases, constraints, input/output format Think out loud: Explain your thought process Start with brute force: Then optimize Consider trade-offs: Time vs. …

Hard-level algorithm interview questions with detailed approach explanations and solutions. Problem 1: Median of Two Sorted Arrays Problem: Find median of two sorted arrays in $O(\log(m+n))$ time. Example: 1Input: nums1 = [1,3], nums2 = [2] 2Output: 2.0 3Explanation: merged = [1,2,3], median = 2 Approach Key Insight: …

Medium-level algorithm interview questions with detailed approach explanations and solutions. Problem 1: Longest Substring Without Repeating Characters Problem: Find length of longest substring without repeating characters. Example: 1Input: s = "abcabcbb" 2Output: 3 3Explanation: "abc" is longest …

Easy-level LLM and Agentic AI interview questions covering fundamentals, prompting, and basic agent concepts. Q1: What is a Large Language Model (LLM)? Answer: Definition: Neural network trained on massive text data to predict next tokens, enabling text generation and understanding. Key Characteristics: Large: Billions …

Hard-level LLM and Agentic AI interview questions covering multi-agent systems, production optimization, and advanced architectures. Q1: Design a multi-agent system with agent communication and coordination. Answer: How Multi-Agent Systems Work: Multiple specialized agents collaborate, communicate, and coordinate to …

Medium-level LLM and Agentic AI interview questions covering agent architectures, RAG optimization, and production systems. Q1: Design and implement a ReAct (Reasoning + Acting) agent. Answer: How ReAct Works: ReAct alternates between reasoning (thinking) and acting (using tools) to solve problems. Pattern: 1Thought: I …

Easy-level protocol and design interview questions covering fundamental concepts. Q1: What is a network protocol and why is it important? Answer: Definition: A set of rules and conventions for communication between network entities. Why Important: Standardization: Different systems can communicate Interoperability: …

Hard-level protocol and design interview questions covering advanced distributed systems and protocol design. Q1: Design a custom protocol for real-time multiplayer gaming. Answer: Protocol Design Message Format Header Fields: Magic Number: Protocol identifier Version: Protocol version Flags: Reliable, ordered, …

Medium-level protocol and design interview questions covering advanced networking and distributed systems concepts. Q1: Explain HTTP/2 improvements over HTTP/1.1. Answer: HTTP/1.1 vs HTTP/2 Multiplexing Benefits: No head-of-line blocking Reduced latency Better bandwidth utilization Fewer connections Q2: Explain gRPC …

Easy-level research interview questions covering fundamental research concepts and methodologies. Q1: What is the scientific method and how do you apply it to technical research? Answer: Application to Technical Research: Observation: System is slow Question: What causes the slowdown? Hypothesis: Database queries are …

Hard-level research interview questions covering advanced methodologies and complex analysis. Q1: Explain Bayesian vs. Frequentist approaches to statistics. Answer: Bayes' Theorem: $$P(\theta|D) = \frac{P(D|\theta) \times P(\theta)}{P(D)}$$ When to Use: Frequentist: Large samples, no prior knowledge Bayesian: Small …