Start by clarifying requirements and scale, then design the core data model for reviews and rewards, and finally detail the review quality scoring and reward calculation services. Emphasize trade-offs between consistency, latency, and cost, and discuss how to prevent abuse and ensure fairness.
Pro tip: Propose a two-phase approach: first, a simple MVP with basic scoring and manual review, then iterate with ML-based scoring and fraud detection. This shows pragmatism and awareness of real-world constraints.
Ask about expected review volume, reward budget, quality scoring criteria, and latency requirements. Establish functional and non-functional requirements.
Define schemas for reviews, users, orders, rewards, and quality scores. Choose appropriate databases (e.g., SQL for transactions, NoSQL for reviews) and discuss indexing and sharding.
Outline how to compute quality scores using signals like length, sentiment, specificity, and helpfulness votes. Discuss batch vs. real-time scoring and ML model integration.
Describe how to map quality scores to dollar rewards, handle budget constraints, and ensure idempotent reward issuance. Discuss payment integration and fraud prevention.
Discuss scaling the system, ensuring eventual consistency between services, and implementing anti-abuse measures like rate limiting and anomaly detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Broke this into write path and read path pretty quickly.
Start by clarifying the scope and non-functional requirements, then design the data model and API endpoints for review submission, scoring, rewards, and browsing. Walk through the end-to-end flow, highlighting trade-offs and scalability considerations, and conclude with how you would handle sorting, pagination, and quality scoring.
Pro tip: Emphasize idempotency and anti-abuse measures for review submission and reward issuance, as these are critical in a food delivery platform like DoorDash where fake reviews and reward fraud are common concerns.
Ask about expected scale (e.g., reviews per item, QPS), latency requirements, and whether reviews are moderated. Clarify if rewards are monetary or points, and if scoring is automated or manual.
Propose schemas for reviews (text, photos, rating, user, item, timestamp, status), scores, rewards, and user balances. Choose appropriate databases (e.g., SQL for transactions, NoSQL for scalability) and discuss indexing for sorting and pagination.
Outline RESTful endpoints: POST /reviews for submission (with idempotency key), GET /items/{id}/reviews with sorting and pagination, and internal endpoints for scoring and rewards. Describe asynchronous processing for scoring and reward issuance.
Explain how to score reviews (e.g., ML model, heuristics based on length, photos, helpfulness votes) and how to issue rewards (e.g., points, badges) upon score threshold. Discuss anti-abuse measures like rate limiting and fraud detection.
Describe pagination strategies (offset vs. cursor-based) and sorting options (by date, rating, helpfulness). Discuss trade-offs and how to optimize queries with indexes or caching.
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 non-functional priorities (e.g., scalability, latency, consistency, fairness, security), then dive into each area: design for high read throughput using caching, replication, and read-optimized data stores; ensure fair and abuse-resistant scoring with rate limiting, anomaly detection, and transparent algorithms; and guarantee idempotent payouts via idempotency keys, transactional outbox, and exactly-once processing. Tie your choices back to business impact and trade-offs.
Pro tip: Emphasize idempotency and fairness as first-class concerns, not afterthoughts—show how you'd instrument and monitor them (e.g., metrics for duplicate payouts, score distribution) to catch issues early.
Ask questions to understand scale, read/write ratio, consistency needs, regulatory constraints, and abuse vectors. Prioritize non-functional requirements like latency, availability, fairness, and auditability.
Propose a read-optimized architecture: caching (CDN, Redis), read replicas, denormalization, and eventual consistency where acceptable. Discuss trade-offs between consistency and latency.
Outline a scoring system with rate limiting, CAPTCHA, anomaly detection, and weighted algorithms to prevent manipulation. Include transparency and appeal mechanisms for fairness.
Use idempotency keys, unique constraints, and transactional outbox pattern to ensure exactly-once payout processing. Discuss reconciliation and dead-letter queues for failures.
Recap key decisions and their trade-offs (e.g., consistency vs. availability). Highlight monitoring, alerting, and metrics to validate non-functional requirements in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran out of time here and kind of speed-ran it.
Start by clarifying requirements and scale, then design each anti-abuse component (rate limiting, deduplication, fake review detection) with clear trade-offs between precision, recall, and latency. Emphasize a layered defense approach, combining real-time and batch processing, and discuss how to measure and iterate on each component.
Pro tip: Demonstrate awareness of adversarial adaptation: attackers will evolve, so build systems that can learn from new abuse patterns and allow quick iteration without redeploying core services.
Ask about expected traffic (e.g., reviews per second), abuse types, and business impact. Define success metrics like false positive rate, detection latency, and coverage.
Propose a distributed rate limiter (e.g., token bucket with Redis) per user/IP/device, with tiered limits and graceful degradation. Discuss trade-offs between strictness and user experience.
Use content hashing (e.g., SimHash) and similarity thresholds to detect near-duplicates. Store hashes in a fast lookup store (e.g., Redis) and consider windowed deduplication to avoid false positives.
Combine rule-based signals (e.g., new account, burst activity) with ML models (e.g., graph-based anomaly detection, NLP for sentiment). Use a scoring system and human-in-the-loop for borderline cases.
Explain how components work together in a pipeline (real-time and batch), and how to monitor effectiveness, handle false positives, and iterate based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.