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Most Frequently Asked Low Level Design(LLD) Interview Questions

Below are the curated list of most commonly asked Low Level Design (LLD) interview problems. Each problem includes a short description and a link to the complete solution with code and class diagrams. Design Parking Lot System The system should handle parking for different vehicle types such as bikes, cars, and trucks. It should manage slot allocation, availability tracking, and entry/exit flow. The design also ensures efficient usage of parking space under varying load conditions. View Solution Design Elevator / Lift System The system should support multiple elevators operating across floors with request handling logic. It focuses on scheduling algorithms to minimize wait time and optimize movement. It also manages direction control and concurrent floor requests. View Solution Design Movie Ticket Booking System The system should allow users to browse movies, select shows, and book seats. It handles seat ...
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Designing a Parking Lot - Low Level Design

Problem Statement Design a parking lot that can handle vehicles entering and leaving while managing parking across multiple floors. Each vehicle should be assigned a suitable parking spot based on its type, and the spot should be freed once the vehicle exits. The design should also support generating a ticket at entry and optionally calculating the parking fee based on the duration of stay. Asked In Companies Amazon Google Microsoft Uber Walmart Flipkart Meta PayPal Oracle Salesforce Adobe Apple Intuit LinkedIn Atlassian Functional Requirements The design should support multiple vehicle types such as bikes, cars, and trucks A vehicle must be assigned a parking spot compatible with its type A parking spot cannot be assigned to more than one vehicle at a time The parking lot should support multiple levels (floors) The design should search and allocate an availa...
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Design an AI Agent Tool Execution Coordinator

Problem Statement Design an autonomous AI Agent Tool Execution Coordinator (similar to the orchestration cores of LangChain, Semantic Kernel, or Claude Desktop agent runtimes). The engine must coordinate agents by parsing structured tool call requests emitted by a Language Model (LLM), invoking corresponding local Java code methods dynamically by name using reflection, appending execution outputs back to the context history, and driving the agentic loop iteratively until a final synthesized text answer is generated. Additionally, to prevent token limit errors in long-running sessions, the engine must perform proactive context-window pruning, dropping the oldest historical messages while strictly preserving the system instructions template at the head of the context. Asked In Companies OpenAI Anthropic Google Microsoft Design Decisions & Patterns Used Modern agentic AI systems use dynamic tool execution to interact with external databas...
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