I jumped straight to writing methods and the interviewer stopped me pretty quickly to ask about my class diagram.
Start by clarifying requirements and scale, then define core entities (Card, Account, Transaction, Statement, Payment) and their relationships. Walk through the lifecycle: issuance, authorization, settlement, repayment, and statement generation, highlighting key design decisions and trade-offs at each stage.
Pro tip: Emphasize idempotency and exactly-once processing for financial transactions, and discuss how you would handle failures and reconciliation to ensure data consistency.
Ask about expected transaction volume, latency requirements, consistency needs, and regulatory constraints. Establish whether the system is for a single bank or multiple issuers.
Identify entities like Card, Account, Transaction, Authorization, Settlement, Statement, and Payment. Define their attributes and relationships (e.g., one Account has many Cards, one Card has many Transactions).
Describe how a card is issued (application, approval, card generation) and how authorization works in real-time (checking credit limit, fraud, and placing holds).
Explain how authorized transactions are settled (batch processing, clearing), how repayments are applied, and how statements are generated periodically with accurate balances.
Discuss trade-offs between consistency and availability, partitioning strategies, and how to scale each component. Mention idempotency, retries, and reconciliation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining a clear data model that separates pending and settled balances, then walk through the state transitions for authorize, settle, and void, explaining how available credit is computed at each step. Emphasize idempotency, consistency, and trade-offs between simplicity and correctness in a distributed system.
Pro tip: Highlight the importance of idempotency keys and handling partial captures or multiple partial settlements, as these are common real-world edge cases that demonstrate production experience.
Describe the core entities: an account with a settled balance and a list of pending authorizations, each with an amount and status. Explain that available credit is calculated as credit limit minus settled balance minus sum of pending authorizations.
When an authorization is created, add a pending authorization record and immediately reduce available credit by the authorized amount. The settled balance remains unchanged.
On settlement, move the amount from pending to settled: decrease pending authorizations and increase settled balance. Available credit is restored by the difference between the authorized and settled amounts (if any), or remains unchanged if full settlement.
When a void occurs, remove the pending authorization and increase available credit by the voided amount. The settled balance is unaffected.
Cover scenarios like partial settlements, multiple partial captures, expiration of authorizations, and idempotency. Discuss trade-offs between strong consistency and availability, and how to handle concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this within the first few minutes.
Start by stating that you would avoid floating-point types due to precision issues, and instead use a fixed-point representation such as integer cents or a decimal type. Then explain the trade-offs between using integers (performance, exactness) and decimals (ease of use, built-in rounding), and mention the importance of considering currency, rounding rules, and scale. Finally, relate it to Amazon's context of high-scale systems where correctness and efficiency matter.
Pro tip: Mention that at Amazon, where systems handle millions of transactions, using integers for monetary amounts is common to avoid floating-point errors and ensure exact arithmetic, but also acknowledge that some languages provide decimal types that handle rounding correctly. This shows you understand both theoretical and practical aspects.
Explain that float and double are binary floating-point types and cannot represent most decimal fractions exactly, leading to rounding errors in financial calculations.
Suggest using integers to represent the smallest unit of currency (e.g., cents) or a decimal type with fixed precision, which avoids floating-point inaccuracies.
Compare integer-based approaches (fast, exact, but requires manual scaling and rounding) with decimal types (easier to use, built-in rounding, but potentially slower and less portable).
Mention that the choice depends on the system's needs: for high-performance, large-scale systems like Amazon's, integers are often preferred; for applications with complex rounding rules, decimals may be better.
State that you would typically use integer cents for monetary amounts in a high-scale environment, but remain open to decimal types if the language and requirements support it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mentioned State pattern for card lifecycle (active, frozen, closed, pending activation) and Strategy for fee and interest calculators.
Start by clarifying the domain: card state transitions (e.g., active, frozen, closed) and fee/interest calculations. Then propose specific design patterns for each concern, explaining how they decouple logic, improve maintainability, and handle complexity. Finally, discuss trade-offs and how your choices align with Amazon's scalability and reliability requirements.
Pro tip: Emphasize that design patterns are tools, not goals—show how they solve concrete problems like extensibility for new fee types or safe state changes, and mention any anti-patterns you'd avoid.
Ask questions to understand the card lifecycle, types of fees/interest, regulatory rules, and expected scale. This ensures your pattern choices are grounded in real needs.
Use the State pattern to encapsulate state-specific behavior and transitions, avoiding complex conditionals. Mention how this supports adding new states without modifying existing code.
Use Strategy to define a family of algorithms (e.g., different interest calculation methods) and make them interchangeable. This allows dynamic selection based on card type, balance, or promotions.
Show how State and Strategy can work together, and mention other patterns like Factory for creating strategies or Observer for notifying state changes. Discuss transaction boundaries and idempotency.
Compare with simpler approaches (e.g., switch statements) and explain when patterns add value. Highlight testability, performance, and alignment with Amazon's principles like ownership and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly did not see this coming as a follow-up.
Start by clarifying the requirements: is this a single-node or distributed system, and what consistency guarantees are needed? Then propose a solution that uses atomic operations (e.g., database transactions with row-level locking, optimistic concurrency control, or distributed locks) to serialize the credit check and update. Finally, discuss trade-offs between consistency, latency, and availability, and mention idempotency and retry handling.
Pro tip: Emphasize that the race condition is best solved by making the check-and-update atomic at the data layer, and that application-level locks are insufficient in distributed systems. Also, mention that you'd consider using a message queue to serialize requests per card if eventual consistency is acceptable.
Ask about the system architecture (monolith vs. microservices, single DB vs. distributed), consistency requirements (strong vs. eventual), and expected load. This shows you understand the problem context before jumping to solutions.
Explain that without proper synchronization, both requests could read the same available credit and both approve, leading to over-limit spending. Highlight the need for atomicity in the check-and-decrement operation.
Suggest using database transactions with SELECT ... FOR UPDATE, optimistic concurrency control (version numbers), or conditional updates (UPDATE ... WHERE available_credit >= amount). For distributed systems, consider distributed locks (e.g., Redis, ZooKeeper) or serializing via a queue.
Compare approaches: pessimistic locking adds latency but ensures strong consistency; optimistic locking may cause retries under contention; distributed locks add complexity and potential single point of failure. Mention that idempotency keys can prevent duplicate processing.
Explain how to handle retries, timeouts, and partial failures. Suggest logging and metrics to detect race conditions, and possibly a reconciliation process to correct any inconsistencies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said interest accrues on the closing balance and got corrected.
Start by defining the core mechanics of interest accrual during a billing cycle, emphasizing the daily balance method and the annual percentage rate (APR) as the basis. Then, connect this to system design by discussing how to model and compute interest accurately in a scalable, event-driven architecture, highlighting trade-offs and edge cases.
Pro tip: Demonstrate awareness of regulatory and business constraints (e.g., compounding frequency, grace periods) and how they influence the data model and system design, showing you can balance technical and domain requirements.
Explain that interest typically accrues daily based on the outstanding principal balance, using the daily periodic rate derived from the APR. Mention that the basis is often the average daily balance or the daily balance method.
State that the correct basis is the annual percentage rate (APR) divided by the number of days in the year (365 or 360) to get the daily rate, applied to the balance each day. Note that compounding may occur daily or monthly depending on the product.
Describe how to represent transactions, balances, and interest accruals in a data model, using event sourcing or a ledger to capture daily balances and compute interest incrementally.
Discuss system design considerations: batch vs. real-time computation, idempotency, handling late-arriving transactions, and ensuring consistency across distributed systems.
Cover scenarios like grace periods, partial payments, mid-cycle rate changes, and how they affect accrual, showing you can handle complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scope of the rewards/cashback feature, then design the system by identifying key components such as earning, redemption, and tracking. Focus on trade-offs between consistency, scalability, and cost, and explain how you would integrate with existing services while ensuring idempotency and fault tolerance.
Pro tip: Emphasize idempotency and exactly-once processing for reward accrual and redemption to prevent duplicate credits or debits, and discuss how you would handle reconciliation and auditing for financial accuracy.
Ask questions to understand the business rules: how rewards are earned (e.g., percentage of purchase), redemption options (cashback, points), expiration policies, and scale (users, transactions per second).
Outline the main components: a rewards service to manage balances, an event-driven pipeline to capture earning events (e.g., from orders), and a redemption service. Consider data stores for balances and transaction logs.
Discuss how to ensure idempotency (e.g., using unique transaction IDs), handle concurrency (e.g., optimistic locking), and maintain consistency across services (e.g., sagas or two-phase commit).
Explain how to scale the system (e.g., sharding by user ID, using queues for asynchronous processing) and ensure fault tolerance (e.g., retries, dead-letter queues, monitoring).
Compare design choices: synchronous vs. asynchronous earning, SQL vs. NoSQL for balances, and centralized vs. decentralized ledger. Justify your recommendations based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.