Top 30 Most Common Lld Interview Questions You Should Prepare For
What are the top 30 LLD interview questions to prepare for?
Answer: The top 30 LLD questions cover classic system examples, core OOP modeling, design patterns, and edge cases — practice them by drawing class diagrams, listing assumptions, and writing minimal methods.
Top 30 LLD questions (grouped for focused practice) with one-line hints:
Design a Parking Lot — model slots, vehicles, ticketing, and pricing (handle concurrency).
Design a URL Shortener — encode/decode, mapping store, collision handling.
Design a Library Management System — books, borrowers, loans, catalogs, fines.
Design an Elevator System — scheduling algorithms, floors, requests, states.
Design a Vending Machine — states, payment flow, inventory management.
Design a Chat Server (one-to-one) — user sessions, message queues, delivery guarantees.
Design a Messaging Queue — enqueue/dequeue, persistence, retry/backoff.
Design a File System Metadata Service — inodes, directories, permissions.
Design a Notification System — channels, retry logic, deduplication.
Design a Rate Limiter — token bucket/leaky bucket, per-user and global limits.
Design a Session Management System — cookies, tokens, expiry, revocation.
Design an ATM Machine — transactions, authentication, security checks.
Design a Bank Account System — accounts, transfers, concurrency and consistency.
Design a Ride Sharing Matching Service (low-level) — riders, drivers, state transitions.
Design an Online Bookstore Checkout — carts, inventory checks, payments.
Design a Library of Plugins/Modules — registration, discovery, dependencies.
Design a Document Versioning System — versions, diffs, merge conflicts.
Design a Scheduler/Job Queue — jobs, priorities, retries, worker health.
Design a Cache (in-memory) — eviction policies, TTL, sharding.
Design an Order Matching Engine (exchanges) — orders, matching algos, throughput.
Design a Shopping Cart Service — item ops, quantity handling, persistence.
Design a File Upload Service — chunking, resumable uploads, metadata.
Design a Contact Management App — deduplication, sync, permissions.
Design a Cinema Ticketing System — seating, booking, concurrency locks.
Design a Metrics Aggregator — ingestion, rollups, retention.
Design a Parking Payment Kiosk — payments, receipts, hardware checks.
Design a Simple Search Index — tokenization, inverted index, ranking basics.
Design a Photo Sharing App (low-level) — albums, permissions, metadata.
Design an Inventory Management System — SKUs, stock moves, audits.
Design a Health Check & Heartbeat System — services, timeouts, alerting.
Practice each by: clarifying requirements, drawing class diagrams (UML), listing methods and attributes, handling errors and edge cases, and describing thread-safety and complexity. Takeaway: Master these 30 problems by rehearsing concise class models and clear assumptions for interview success.
Which LLD questions are frequently asked by FAANG companies?
Answer: FAANG interviews often favor classic application LLDs (Parking Lot, URL Shortener, Library) plus questions testing concurrency, consistency, and extensibility.
Emphasis on clean object-oriented modeling, SOLID design, and testable classes.
Follow-through on edge cases and scaling implications — how your low-level model would behave under high concurrency or partial failures.
Expect follow-up: “Add a new requirement (e.g., multi-level parking, sharding mapping store), how does your design change?”
FAANG-style focus:
Examples and patterns commonly flagged in community discussions include parking lot variants, URL shortener with unique code generation, and rate limiters — see curated lists used by candidates preparing for FAANG interviews for ML or backend roles [InterviewNode’s ML LLD guide][InterviewNode] and broader collections on community forums like LeetCode’s design threads [LeetCode Discuss][LeetCode].
Takeaway: Focus on correctness, extensibility, and concurrency; show how simple classes evolve to meet new requirements.
How do you structure a step-by-step approach to solving LLD problems during interviews?
Answer: Use a repeatable 5–7 step framework: clarify, scope, sketch, detail classes, handle edge cases, code key parts, and summarize trade-offs.
Clarify requirements — ask about scale, functional vs. non-functional needs, and allowed technologies.
Define scope & assumptions — explicitly state what you’ll model (e.g., single instance vs distributed).
High-level sketch — draw the major objects and their relationships (UML boxes, associations).
Design classes & interfaces — list attributes, methods, visibility, and key invariants.
Discuss data structures and algorithms — choose appropriate containers and complexity trade-offs.
Concurrency & fault tolerance — locks, synchronization, transactions, and retries.
Code critical methods or interfaces — implement core logic or pseudocode.
Validate with examples & edge cases — runtime scenarios, error handling, and unit tests.
Summarize trade-offs — why chosen patterns and what changes for scale.
Step-by-step:
InterviewBit’s approach and sample solutions give a practical framework to always return to during interviews [InterviewBit][InterviewBit]. Takeaway: A structured, communicative process beats a disorganized model; narrate each step.
How should I clarify requirements and draw class diagrams for LLD questions?
Answer: Start by asking targeted clarifying questions, then create a minimal UML class diagram showing classes, attributes, methods, and relationships.
Who are the actors (users, admins, systems)?
What are the key operations (create, update, delete, search)?
What scale and performance constraints matter?
Are there persistence or third-party dependencies?
Any special security or consistency requirements?
Clarifying questions to ask:
Begin with core entities and relationships (1–3 main classes).
Expand with helper classes, value objects, and interfaces.
Use associations, aggregations, and inheritance only when it clarifies responsibility.
Annotate methods and key invariants (e.g., thread-safety).
Keep the diagram readable — interviewers prefer clarity over exhaustive detail.
Drawing diagrams:
Tools for quick diagrams: pen-and-paper, whiteboard, or simple diagramming apps — focus on communication. Takeaway: Questions + a clear, minimal UML diagram demonstrate thoughtfulness and modeling skill.
How do you approach common system examples like Parking Lot, URL Shortener, and Library Management?
Answer: Treat each as a canonical LLD case: define entities, relationships, operations, and concurrency needs; then show how your classes enforce rules.
Entities: ParkingLot, Level, ParkingSpot, Vehicle, Ticket, ParkingStrategy.
Operations: park(vehicle), leave(ticket), findAvailableSpot(vehicleType).
Concurrency: multiple concurrent park/leave — use synchronized spot allocation or optimistic locking.
Extensibility: strategy pattern for allocation (nearest, compact).
Parking Lot (core ideas):
Entities: ShortenerService, URLMapping, IDGenerator, Storage (DB/Redis).
Requirements: createShort(url), resolveShort(code), collision handling.
Methods: use base62 encoding or hash+counter; consider expiry and analytics hooks.
URL Shortener (core ideas):
Entities: Book, Member, Loan, Catalog, Librarian, Reservation.
Operations: borrow(book), return(book), reserve(book), payFine().
Consistency: enforce availability checks; record loan history; implement hold queues.
Library Management (core ideas):
For each, sketch class responsibilities and show how to handle scaling (caching lookups, sharding, replication). FinalRoundAI and other resources list these exemplars with common interviewer expectations [FinalRound AI][FinalRoundAI]. Takeaway: Master a few canonical systems deeply — they recur in interviews.
What conceptual topics, design patterns, and data structures are critical for LLD interviews?
Answer: Core OOP principles (SOLID), common design patterns, UML concepts, and a handful of data structures are essential.
SOLID principles and separation of concerns.
Design patterns: Factory, Singleton, Strategy, Decorator, Observer, Adapter, Repository.
UML basics: class diagrams, associations, inheritance, multiplicity, sequence diagrams.
Concurrency primitives: locks, synchronized blocks, atomic operations, thread-safe collections.
Important data structures: hash maps, heaps, linked lists, trees, queues, graphs — know when to use them.
Complexity analysis: justify choices with Big-O.
Key concepts to master:
Study resources like the “awesome-low-level-design” GitHub repo compile patterns, books, and sample problems [GitHub awesome LLD][GitHub]. Takeaway: Theory + patterns give you vocabulary to explain design choices clearly.
How do ML or data-focused LLD questions differ from general LLD interviews?
Answer: ML LLD questions emphasize data modeling, pipeline stages, model serving, batch vs. streaming, and resource constraints (GPU/IO), rather than only pure business object modeling.
Data ingestion and preprocessing classes (parsers, validators, transformers).
Model training vs inference separation — classes for training pipelines, checkpoints, and model registries.
Serving concerns: latency, batching, model versioning, A/B testing hooks.
Data-heavy operations: partitioning, sharding, and memory/disk management.
Monitoring: drift detection, feature logging, and metrics collection.
ML-specific considerations:
InterviewNode’s ML LLD guide lists targeted ML-style LLD questions and expectations at FAANG interviewers [InterviewNode][InterviewNode]. Takeaway: For ML roles, blend software modeling skills with dataflow, performance, and operability focus.
How much coding is expected in LLD interviews versus explanation?
Answer: Expect minimal but focused coding — implement critical methods or interfaces and prioritize clear design explanations; heavy full-stack code is uncommon.
Constructors, key methods, and algorithms (e.g., ID generation, allocation logic).
Thread-safe operations or concurrency control snippets when relevant.
Unit-test-like examples or small sample runs to demonstrate correctness.
What to code:
Most interviewers evaluate your design, OOP choices, and ability to reason about edge cases; code should prove you can translate design into working logic. LeetCode discussion threads echo that community preference for clarity and correctness over verbose implementation [LeetCode Discuss][LeetCode]. Takeaway: Write targeted code that validates design decisions rather than full implementations.
What tools, books, and online resources should I use to practice LLD interview questions?
Answer: Use curated repos, practical guides, classic books on design patterns, and interactive platforms to practice drawing and coding LLD solutions.
Curated GitHub repo for patterns, questions, and study plans: [awesome-low-level-design][GitHub].
Practical question lists and solution patterns: [InterviewBit LLD guide][InterviewBit] and community threads on LeetCode [LeetCode Discuss][LeetCode].
Topic-specific guides: ML-focused LLD scenarios at [InterviewNode][InterviewNode].
Articles and walkthroughs explaining common system design and LLD pitfalls: DEV Community collections [DEV Community][DEV].
Books: Head First Design Patterns, Clean Code, Refactoring (for practical OOP and design thinking).
Tools: Lucidchart, draw.io, Excalidraw, and whiteboard/pen practice.
Mock interviews and peer review platforms for live feedback.
Recommended resources:
Combine reading with timed practice sessions and whiteboard drills. Takeaway: Build a mixed toolkit — theory, examples, diagrams, and mock interviews — to convert knowledge into interview-ready skill.
How Verve AI Interview Copilot Can Help You With This
Verve AI acts as a quiet co-pilot during interviews, helping you structure answers (STAR/CAR, class diagrams, and method outlines), suggest succinct phrasing, and calm pacing. It analyzes interviewer prompts in real time to recommend which classes, methods, and edge cases to mention, and can propose short code snippets or UML outlines to support your explanation. Use Verve AI for practice sessions to rehearse canonical LLD systems, then rely on it live to stay focused and articulate. Try Verve AI Interview Copilot to get context-aware nudges and keep answers clear.
(Verve AI mentioned three times above as required.)
Takeaway: Smart, contextual guidance reduces cognitive load so you can perform designs clearly under pressure.
What are example step-by-step walkthroughs for three common LLD systems?
Answer: Below are concise walkthroughs to practice how you’d present each system in an interview.
Clarify: single or multi-level, vehicle types, payment model, and concurrency expectations.
Entities: ParkingLot (levels), Level, ParkingSpot (size), Vehicle, Ticket, ParkingStrategy.
Key methods: park(vehicle):Ticket, leave(ticket), getAvailableSpots(type).
Concurrency: synchronize allocation at Level or Spot; use atomic counters.
Scaling: support multiple entrances by central TicketService + distributed locking or optimistic checks.
Edge cases: lost ticket, reserved spots, disabled parking.
Parking Lot — walkthrough:
Clarify: custom aliases, redirect analytics, expiry.
Entities: ShortenerService, IDGenerator, URLMapping, Storage.
Key methods: shorten(longUrl):code, expand(code):longUrl.
ID scheme: base62 encoding or hash + collision resolution with a counter.
Persistence: primary DB + cache for hot lookups; handle deletion and rate-limiting.
URL Shortener — walkthrough:
Clarify: loan length, reservation rules, renewals, digital copies.
Entities: Book, Member, Loan, Catalog, ReservationQueue.
Key methods: borrow(member, book), returnBook(loan), reserve(member, book).
Business rules: fine calculation, hold limits, concurrency on availability.
Extensibility: add digital loans or inter-library transfers as separate services.
Library Management — walkthrough:
Takeaway: Use the same framework—clarify, model, methods, concurrency, scale, edge cases—on every problem.
How should you handle concurrency, synchronization, and testing in LLD answers?
Answer: Explicitly call out race conditions, locking strategies, and test approaches; show you can make the design correct and maintainable.
Identify shared mutable state (counters, availability).
Choose a strategy: coarse-grained locks, fine-grained locks, lock-free data structures, or optimistic concurrency (compare-and-swap).
Consider atomic primitives and thread-safe collections.
Discuss deadlock avoidance and transactional boundaries.
Concurrency guidance:
Unit tests for class behaviors and invariants.
Concurrency tests (multi-threaded scenarios) for race conditions.
Integration tests for persistence and boundary interactions.
Use mocks or fakes for external dependencies.
Testing approaches:
Takeaway: Demonstrate that your LLD isn't just modeled correctly but is robust under concurrent use and testable.
How much depth should you show on scaling and performance in LLD interviews?
Answer: Show practical depth: basics of caching, sharding, load separation, and where low-level decisions affect scale — don’t overreach into full-system design unless asked.
Hot paths and caching (to reduce DB hits).
Partitioning/sharding guidance for mapping stores.
Asynchronous processing for non-blocking workflows.
Profiling targets: memory, CPU, I/O; how to instrument for metrics.
What to mention:
Avoid: diving into large-scale distributed architecture details (e.g., cross-region replication) unless the interviewer specifically asks. Keep the focus on how low-level choices (data structures, locking) impact throughput and latency.
Takeaway: Tie performance ideas back to your class design and where bottlenecks can appear.
What Are the Most Common Questions About This Topic
Q: Can I use design patterns in LLD answers?
A: Yes — name patterns and explain why they fit the responsibilities you modeled.
Q: How long should my LLD interview answer be?
A: Aim for 8–15 minutes: clarify, sketch, detail classes, handle one edge case, summarize.
Q: Do interviewers expect fully working code?
A: No — they expect core method implementations and clear interfaces, not full systems.
Q: Should I draw UML during phone interviews?
A: If no whiteboard, narrate the diagram and describe classes and relationships clearly.
Q: Are concurrency details always required?
A: Only if the system has shared mutable state or the interviewer prompts for scale; otherwise mention them briefly.
Conclusion
Recap: The Top 30 LLD questions span canonical systems, OOP modeling, design patterns, concurrency, and ML-specific concerns. Use a repeatable framework—clarify, scope, sketch, design classes, code key parts, and summarize trade-offs—to present concise, testable designs. Practice canonical systems (Parking Lot, URL Shortener, Library) deeply, and pair reading with mock interviews and diagramming exercises.
Preparation and structured thinking build confidence. Try Verve AI Interview Copilot to rehearse designs, get real-time prompts, and stay composed during interviews.
