I started with the class structure which felt okay, Vehicle as a base with subclasses, Ticket tied to a ParkingSpot, that kind of thing.
Start by clarifying requirements and scale (e.g., number of levels, spots, vehicle types, concurrent users) to bound the design. Then define core entities and their relationships, focusing on clean APIs for parking, unparking, and availability queries, and finally walk through the parking/unparking flow including ticketing and payment rules.
Pro tip: Demonstrate concurrency awareness by discussing how to handle simultaneous parking requests for the same spot (e.g., using locks or atomic operations) and how to ensure consistency in availability counts.
Ask about expected scale (number of spots, levels, daily transactions), vehicle types, pricing rules (hourly/daily), and any special features like reserved spots or EV charging.
Identify main classes: ParkingLot, Level, ParkingSpot, Vehicle, Ticket, Payment, EntryGate, ExitGate. Describe how they relate (e.g., ParkingLot has Levels, Level has Spots, Ticket links Vehicle and Spot).
Specify key methods: park(vehicle, entryGate), unpark(ticket, exitGate), getAvailability(vehicleType, level). Define data fields for each class and how availability is tracked (e.g., counts per spot type per level).
Explain step-by-step: vehicle enters, gets ticket with timestamp, spot assigned, ticket stored. On exit, calculate fee based on duration and pricing rules, process payment, free spot, update availability.
Discuss locking or atomic operations for spot assignment, handling full lot, lost tickets, payment failures, and scaling with multiple entry/exit gates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about optimistic locking versus a reservation queue.
Start by clarifying the requirements and constraints, then propose a solution that uses atomic operations (e.g., conditional writes) to ensure only one vehicle can claim a spot. Discuss trade-offs between consistency and availability, and mention how to handle failures and retries.
Pro tip: Emphasize idempotency and optimistic concurrency control, as these are crucial for building reliable distributed systems at scale. Also, relate your answer to Amazon's leadership principles like Customer Obsession and Ownership.
Ask about scale, latency requirements, consistency needs, and failure scenarios to understand the problem fully.
Propose using atomic operations such as conditional writes (e.g., DynamoDB conditional put) or distributed locks to ensure only one claim succeeds.
Discuss trade-offs between strong consistency and eventual consistency, and how to handle conflicts (e.g., optimistic locking with version numbers).
Explain how to make operations idempotent and implement retries with backoff to handle transient failures.
Mention monitoring for contention and scaling strategies like sharding or partitioning to reduce hotspots.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scope and requirements, then walk through the full-capacity scenario step-by-step, covering detection, user experience, and backend handling. Emphasize trade-offs between consistency, availability, and cost, and how you would ensure a seamless experience for drivers.
Pro tip: Proactively discuss how you would handle edge cases like concurrent entry attempts or sensor failures, and tie your decisions back to business metrics like customer satisfaction and revenue loss.
Ask clarifying questions about the parking lot size, expected peak times, and whether the system is for a single lot or a network. State your assumptions to set the context.
Explain how the system determines the lot is full, such as through entry/exit counts, sensors, or real-time occupancy tracking, and how to handle discrepancies.
Describe the user experience when the lot is full: updating signage, mobile app notifications, and possibly redirecting to nearby lots or waitlists.
Discuss how to maintain accurate counts, handle concurrent requests, and ensure data consistency across distributed components.
Analyze trade-offs between different approaches (e.g., strict consistency vs. availability) and how the solution scales with multiple lots or high traffic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scope and the ticket's role, then outline a layered failure-handling strategy that includes detection, graceful degradation, recovery, and prevention. Emphasize how you would balance customer experience, security, and operational efficiency, aligning with Amazon's customer obsession and ownership principles.
Pro tip: Proactively discuss trade-offs between strict enforcement and customer trust, and propose a feedback loop to continuously improve failure handling based on real-world data.
Ask questions to understand the system's context, such as whether tickets are physical or digital, the scale of operations, and the cost of errors. State your assumptions to ensure alignment.
Enumerate potential failure scenarios (lost, damaged, stolen, unreadable tickets) and assess their impact on users, revenue, and operations.
Propose methods to detect invalid tickets (e.g., barcode scanning, manual entry) and recovery options (e.g., lookup by license plate, appeals process) that balance security and convenience.
Describe fallback procedures when primary systems fail, such as manual verification or temporary passes, ensuring the system remains usable.
Outline how to track failure incidents, analyze patterns, and feed insights back into the system to reduce future occurrences and improve handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.