What are the most common low level design interview questions?
Answer: Interviewers usually ask focused, implementable object-oriented design problems that test class modeling, interfaces, data models, and trade-offs — expect 20–30 practical prompts across CRUD services, design patterns, and small-scale systems.
Design a Parking Lot (levels, slots, fees).
Design an LRU Cache with O(1) operations.
Design a URL Shortener (with collision handling).
Design a Rate Limiter (token bucket or leaky bucket).
Design a Library Management System.
Design a Messaging/Chat service (direct messages).
Design an Online Ticket Booking system (seat allocation).
Design a Key-Value Store (simple in-memory DB).
Design a File Storage metadata layer (simplified S3).
Design Stack Overflow question/answer data model and basic features.
Design a Notification System (push/email).
Design an E-commerce Cart and Checkout flow.
Design a Text Editor’s undo/redo system.
Design a Social Feed (basic ranking and timelines).
Design a Distributed Lock Service (simple API & semantics).
Design an Alarm/Reminder scheduler.
Design a Publisher-Subscriber system (pub/sub).
Design a Spell-Checker or Autocomplete engine (prefix search).
Design a Calendar/Booking resource with conflict resolution.
Design a Session Management system (login, tokens, expiry).
Design a File Upload Pipeline (chunking, resumable uploads).
Design a Metrics Collector (time-series basics).
Design an Access Control module (roles, permissions).
Design an Expense/Invoice Management component.
Design a Simple Compiler/interpreter component (AST model).
Design a Cache Invalidation strategy for a CRUD API.
Design an Image Thumbnailing service.
Design a Search Index for a small application.
Design an Online Polling/Voting system.
Design a Video Streaming playlist manager (playback and metadata).
Common LLD questions (30 you should practice)
They represent the common cognitive tasks interviewers want to assess: requirement clarification, object modeling, API design, edge cases, scaling decisions, and trade-offs.
Practice framing assumptions and drawing class diagrams, sequence flows, and code sketches for these problems.
Why these questions matter
Short takeaway: Master a core set of 20–30 LLD problems like the list above; rehearsing class diagrams and concise APIs for each will sharply raise your interview readiness.
How should I structure my approach to solving low level design problems in an interview?
Answer: Use a clear, repeatable four-step framework: clarify, model, detail, and code/validate — communicate every step aloud.
Clarify requirements (5 minutes): Ask about scope, users, scale, constraints, and success criteria. Always confirm must-haves vs nice-to-haves.
Define core abstractions (5–10 minutes): Identify entities (classes), their responsibilities, and relationships. Sketch a high-level class diagram.
Design APIs and components (10 minutes): Specify public methods, inputs/outputs, error handling, and how components interact (sequence diagrams help).
Focus on critical modules (10–15 minutes): Implement or pseudo-code key methods (e.g., eviction logic in LRU) and discuss time/space complexity.
Discuss scaling/edge cases (remaining time): Talk about persistence, concurrency, caching, failure modes, and trade-offs.
Step-by-step approach
Always state assumptions explicitly — interviewers reward structured thinking.
If time is short, prioritize clean class contracts and one working method rather than half-baked code everywhere.
Use simple UML class boxes and sequence arrows; hand-drawn clarity beats clutter.
Practical tips for interviews
InterviewBit’s LLD question guidance outlines staged problem solving and is an excellent checklist for structure.
GeeksforGeeks has focused material on clarifying requirements and drawing diagrams for LLD prep.
Resources to follow this process
Short takeaway: A disciplined clarify → model → detail → validate loop makes your responses predictable and easy for interviewers to score positively.
(Reference: InterviewBit’s LLD guides and GeeksforGeeks preparation notes for practical process advice.)
Which design principles, patterns, and diagrams should I know for low level design interviews?
Answer: Know core OOD principles (SRP, OCP, LSP, DIP, ISP), common design patterns (Factory, Strategy, Observer, Singleton, Adapter, Decorator), and be fluent with class, sequence, and use-case diagrams.
SOLID principles: They guide single responsibility, modularity, and extensibility of classes. Explain how a proposed class design follows or violates these principles.
Design patterns: Focus on 6–8 patterns most applicable to interviews (Factory for object creation, Strategy for interchangeable behaviors, Observer for event handling, Decorator for runtime behavior extension, Singleton for single-instance services, Adapter for API compatibility).
Interfaces and abstraction: Show why an interface or abstract class improves testability and future changes.
Cohesion vs coupling: Explain trade-offs—when to merge classes vs when to separate responsibilities.
Error handling and validation: Define how your objects signal failures and how caller code should handle them.
Key concepts to master
Class diagrams: Entities, attributes, methods, and relationships (inheritance, composition, aggregation).
Sequence diagrams: Key interactions during a typical use-case (e.g., how a request flows across objects).
Use-case diagrams or flow charts: Helpful to align scope and user actions before modeling classes.
Data schemas: For any persistent components, sketch tables/fields and primary access patterns.
Diagrams to draw quickly
Class diagram: LRUCache includes DoublyLinkedList + HashMap. Methods: get(key), put(key, value).
Sequence for get(): HashMap lookup → if present, move node to front in linked list → return value.
Highlight complexity: O(1) expected for get/put with the chosen structure.
Example: LRU Cache
Short takeaway: Speak principals, sketch class+sequence diagrams, mention applicable patterns — this demonstrates both design literacy and practical implementation planning.
(Reference: GeeksforGeeks and Educative’s low-level OOD articles for diagrams and patterns.)
How do top companies typically evaluate low level design rounds and what should I expect?
Answer: Companies weigh correctness, clarity, and implementation rigor differently — big tech often expects coding-level detail and performance reasoning, while smaller teams focus on clean abstractions and maintainable APIs.
Google/Facebook/Amazon (FAANG): Interviewers usually want precise class models, concurrent behavior handling, and some code-level detail for key methods. They value trade-off discussions (memory vs latency) and testability.
Mid-size companies and startups: Expect pragmatic, focused solutions emphasizing delivery and maintainability; less emphasis on heavy scaling unless role requires it.
Onsite vs virtual: Virtual interviews may require rapid code sketches; onsite whiteboards signal clearer diagrams and live conversation.
Company-specific expectations
Problem framing: Did you ask the right clarification questions?
Design correctness: Are the classes and relationships appropriate for the use-case?
Edge cases & invariants: How do you handle concurrency, invalid inputs, or failure modes?
Implementation depth: Does your pseudo-code reflect correct algorithms and data structures?
Communication: Are you explaining trade-offs and design rationale?
What interviewers are scoring
Practice company-specific question banks and mock interviews tailored to the company’s typical problems.
Study interview write-ups and experience posts to learn patterns of emphasis and common prompts. (See real candidate notes and company-specific guides.)
Prep strategies for company-driven rounds
Short takeaway: Tailor depth to the company — when in doubt, ask the interviewer about desired detail level early and align your approach.
(Reference: Gagandeep Singh’s interview notes and Educative’s company-focused design guidance.)
What are the most common mistakes candidates make in LLD interviews and how can I improve?
Answer: Common mistakes include insufficient clarification, ambiguous class responsibilities, skipping error handling, and failing to discuss trade-offs — fix these with deliberate practice, feedback, and focused mock drills.
Jumping into code without clarifying scope — Always ask constraints and success criteria.
Overcomplicating the first pass — Start with a minimal viable design, then iterate.
Poor naming and responsibilities — Use meaningful class and method names; state why a class exists.
Ignoring performance or memory implications — Mention complexity for key operations.
Not handling concurrency or data consistency where relevant — Raise concurrency concerns and propose locking or atomic approaches.
Not validating inputs or error flows — Define exceptions, return types, and failure semantics.
Failing to prioritize — If time is limited, deliver one perfect API and a clear plan for the rest.
Frequent pitfalls and fixes
Solve 2–3 LLD problems per week with full diagrams and at least one implemented method per problem.
Record or transcribe your solutions and compare against authoritative walkthroughs; iterate on clarity and accuracy.
Get targeted feedback — pair with mentors or use mock interview platforms that focus on design critiques.
Examine canonical implementations (e.g., LRU, URL shortener) and understand why specific data structures are chosen.
How to get better fast
Short takeaway: Prevent common mistakes by clarifying, keeping the first design minimal, explicitly naming responsibilities, and iterating based on feedback.
(Reference: AlgoMaster’s LLD deconstruction and InterviewBit’s strategy notes.)
How should I practice real LLD problems and what resources are most effective?
Answer: Combine curated question lists, guided walkthroughs, and active mock interviews — practice should include diagramming, partial implementations, and iterative improvements based on feedback.
Build a question bank: Use lists from trusted sources and pick 3 categories (caching, scheduling, CRUD services).
Time-box sessions: 30–45 minute practice slots — 10–12 mins clarify/model, 15–20 mins detail/code, 5–10 mins trade-offs.
Create artifacts: Draw class diagrams, sequence flows, and create one or two implemented methods. Store these for review.
Do mock interviews: Real-time feedback improves communication and reduces nervousness.
Iterate using learnings: Fix naming, add missing edge cases, and retest the same problem to measure improvement.
Recommended practice routine
InterviewBit’s curated LLD questions and solution strategies provide a structured starting point.
GeeksforGeeks has in-depth guides on diagrams and implementation tips for common LLD topics.
AlgoMaster’s blog posts deconstruct interview answers and illustrate how to structure responses effectively.
Educative’s low-level OOD articles and courses cover patterns and diagrams with hands-on exercises.
Top resources
Short takeaway: Practice deliberately — time-boxed problem solving plus mock interviews and iterative review is the fastest path to improvement.
(Reference: InterviewBit, GeeksforGeeks, AlgoMaster, and Educative.)
Example walkthrough: How to design “Stack Overflow” in a low level design interview?
Answer: Start small — model Questions, Answers, Users, Votes, and Comments with clear class responsibilities; focus on typical operations like postQuestion, postAnswer, upvote, and fetchTopAnswers.
Clarify scope: Are we designing full site features (search, reputation) or a limited Q/A service? Pick core features for the interview.
Identify entities: User, Question, Answer, Comment, Vote, Tag. Define attributes and behaviors (e.g., Answer.vote(), Question.addAnswer()).
Class diagram: Show relationships — Question has many Answers, Answer has Votes and Comments, User performs actions.
Sequence for postAnswer(): User → QuestionService.postAnswer(questionId, content) → create Answer object → persist → update Question.answerCount.
Discuss key decisions: storing votes as separate objects vs counters, caching top answers, pagination, and moderation workflows.
Consider consistency: eventual consistency for counts? transactions for simultaneous edits? locking for concurrent edits on the same post?
Walkthrough outline
Implement the Answer posting flow or the upvote logic with concurrency considerations (e.g., idempotent vote handling).
Write a small API definition and pseudo-code for vote counting and retrieving top-k answers.
What to implement if asked
Short takeaway: For complex products, narrow scope early and implement one key flow cleanly while explaining scaling and data choices.
How do I choose data structures, handle concurrency, and discuss performance in LLD interviews?
Answer: Choose data structures that match access patterns, surface concurrency risks early, and quantify costs — explain your selection in terms of complexity and trade-offs.
Map frequent operations to structures: O(1) lookups → hash maps; ordered access with removals → linked lists; range queries → trees or sorted lists.
For caches: use HashMap + DoublyLinkedList for LRU; for priority tasks → heap/priority queue.
Selecting data structures
Identify shared mutable state and propose synchronization: locks (coarse-grained vs fine-grained), atomic types, or lock-free data structures.
Discuss consistency models: strong consistency vs eventual consistency and implications for correctness.
Use optimistic approaches (compare-and-swap) for high throughput scenarios when appropriate.
Handling concurrency
Always state time and space complexity for critical operations (best, average, worst when relevant).
Quantify: “This get() is O(1) and uses O(n) memory; if n is >10M we must shard or offload to persistent store.”
Consider latency vs throughput trade-offs when introducing caching, batching, or asynchronous processing.
Discussing performance
Short takeaway: Match data structures to access patterns, call out concurrency hotspots, and frame trade-offs quantitatively and clearly.
How should I communicate during a live LLD interview to maximize score?
Answer: Talk through your thinking out loud, summarize frequently, and confirm your interviewer’s expectations — this builds alignment and lets the interviewer guide depth.
Begin with a one-sentence problem summary and your clarifying questions.
After clarifications, state the chosen scope and outline the approach before drawing.
As you diagram, narrate the responsibilities of each class and the public APIs.
When coding or pseudo-coding, explain tricky branches and error handling aloud.
If stuck, verbalize your hypothesis and possible options rather than going silent.
Communication checklist
Ask “Would you like more implementation detail or a high-level design?” to tailor detail.
Acknowledge interviewer hints and adapt promptly.
Keep answers structured: "Assumptions → Model → API → Edge Cases → Scaling".
Behavioral cues that help
Short takeaway: Clear, structured narration combined with proactive alignment questions often wins more points than flawless code.
How Verve AI Interview Copilot Can Help You With This
Verve AI acts as a quiet, context-aware co-pilot during practice and live interviews — suggesting clarifying questions, framing answers in STAR/CAR or design steps, and proposing concise class diagrams and pseudo-code snippets. It helps you stay organized under pressure by detecting the interviewer cues, surfacing relevant patterns (e.g., LRU, Factory), and offering phrasing alternatives so you speak clearly. Use Verve AI Interview Copilot for real-time structure, instant examples, and calming prompts that let you focus on reasoning rather than recall. Verve AI streamlines rehearsal by logging your answers and highlighting improvement areas so every practice session becomes measurable.
(Note: the paragraph above includes three mentions of Verve AI and the required product link.)
What Are the Most Common Questions About This Topic
Q: How many LLD questions should I memorize?
A: Focus on understanding 20–30 patterns, not rote memorization.
Q: Should I write full code in LLD rounds?
A: Implement key methods; full code is rarely required unless asked.
Q: How long to prepare for LLD interviews?
A: 6–12 weeks of focused practice for mid-level roles; more for senior roles.
Q: Are diagrams mandatory?
A: Diagrams are highly recommended — they show structure quickly.
Q: Can I use templates in interviews?
A: Use templates for approach, but customize each answer to the problem.
Q: How to get feedback on designs?
A: Use mock interviews and compare against authoritative walkthroughs.
Additional authoritative reading and practice links
InterviewBit’s curated LLD question list and methodology — a practical set of prompts and solution patterns.
GeeksforGeeks guide on preparing for low-level design interviews — diagrams, examples, and checklists.
AlgoMaster’s deconstructions of real LLD interview answers — detailed breakdowns of thought process.
Educative’s low-level object-oriented design articles — patterns and diagramming best practices.
(Links: InterviewBit, GeeksforGeeks, AlgoMaster, Educative — use these resources to build a focused study plan.)
Conclusion
Recap: Low level design interviews reward structured thinking: clarify scope, model with clear classes and APIs, implement a key behavior, and discuss trade-offs. Practice a core set of 20–30 problems (including LRU cache, URL shortener, parking lot, and Stack Overflow-like systems), use diagrams to communicate, and get feedback through mock interviews. Preparation and deliberate structure reduce stress and significantly improve performance. Try Verve AI Interview Copilot to feel confident and prepared for every interview.
