Start by clarifying requirements and scale, then propose a hybrid architecture that combines push for active users and pull for inactive users, with a fan-out service and caching layer. Emphasize trade-offs between latency, cost, and complexity, and discuss how to handle hot users and celebrity problem.
Pro tip: Quantify the impact of design choices: e.g., 'Using push for all users would require 200k * 100M = 20T writes per second, which is infeasible; hybrid reduces this to manageable levels.' This shows you can reason with numbers.
Confirm functional and non-functional requirements: 100M MAU, 200k writes/s, 2M reads/s, p99 <200ms. Ask about content types, personalization, and delivery guarantees.
Propose a hybrid push-pull model: push for active users (e.g., last 24h) and pull for others. Include components: ingestion, fan-out service, feed cache, and ranking service.
Detail fan-out service (e.g., using Kafka and workers), feed storage (e.g., Redis for push, Cassandra for pull), and ranking (ML models for personalization). Address hot users and celebrity problem.
Explain how to achieve 2M reads/s with <200ms latency: use caching, CDN, read replicas, and precomputed feeds. Discuss sharding and partitioning strategies.
Discuss trade-offs: push vs pull (latency vs cost), consistency vs availability. Mention monitoring, A/B testing, and fallback mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining fan-out-on-write and fan-out-on-read, then compare their trade-offs in terms of latency, cost, and complexity. Explain that the decision depends on factors like read/write ratio, user activity patterns, and consistency requirements, and give a concrete example from a Yelp-like feed (e.g., business updates or reviews).
Pro tip: Emphasize that the choice is not binary; hybrid approaches (e.g., fan-out-on-write for active users, fan-out-on-read for inactive) are common in production systems. Also, mention that data scientists should consider how the choice impacts feature freshness and model training pipelines.
Clearly explain fan-out-on-write (pre-compute feeds on write) and fan-out-on-read (compute feeds on read), including their basic mechanics.
Discuss latency, throughput, storage cost, and complexity for each approach, highlighting when each excels (e.g., write-heavy vs read-heavy).
List key factors that influence the choice: read/write ratio, user base size, activity distribution, consistency needs, and cost constraints.
Relate the decision to Yelp's feed (e.g., business updates, reviews, photos) and typical user behavior (many reads, fewer writes, power users).
Suggest a hybrid approach that balances trade-offs, such as fan-out-on-write for active users and fan-out-on-read for others, and mention monitoring and iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked on idempotency for the retract endpoint specifically.
Start by clarifying the system's purpose and key requirements, then outline the API endpoints with their HTTP methods and schemas, and finally discuss idempotency strategies. Emphasize how the design supports data science use cases like feedback loops and content lifecycle management.
Pro tip: For a data science role, highlight how the API design enables experimentation and model improvement by ensuring reliable data collection and idempotent operations. Mention that idempotency keys should be generated client-side and stored server-side with a TTL to prevent replay attacks.
Ask clarifying questions about the system's scale, expected clients, and data science needs. Define the core operations: publish, subscribe, feed retrieval, acknowledge, retract, and feedback logging.
For each operation, specify the HTTP method, path, request/response JSON schemas, and status codes. Ensure consistency in naming and error handling.
Describe how to make operations idempotent, especially for publish, acknowledge, retract, and feedback. Use idempotency keys for POST/PUT requests and ensure GET/DELETE are naturally idempotent.
Detail the pagination strategy (e.g., cursor-based) for the feed endpoint, including parameters and response metadata. Discuss how to handle large feeds and ordering.
Explain how the API design supports data collection for model training, A/B testing, and feedback loops. Mention logging, monitoring, and analytics integration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product goals and constraints, then propose a modular ranking pipeline where each constraint is a post-processing layer after the core relevance model. Explain how you would implement each layer (deduplication, diversity, freshness, caps) with specific algorithms and metrics, and discuss trade-offs and evaluation.
Pro tip: Emphasize that constraints should be applied as soft penalties or hard filters depending on business impact, and always A/B test their effect on long-term user engagement and retention, not just immediate CTR.
Ask about the feed's purpose (e.g., home feed, search), user expectations, and business metrics (e.g., engagement, retention). Define what 'deduplication', 'diversity', 'freshness', and 'caps' mean in this context.
Outline a relevance model (e.g., learning-to-rank) that scores items based on user-item affinity. This serves as the base ranking before applying constraints.
Describe each constraint as a post-processing step: deduplication (e.g., by business ID or content similarity), diversity (e.g., MMR or category quotas), freshness (e.g., time decay or hard recency filter), and daily caps (e.g., per-user per-category limits).
Discuss how constraints interact (e.g., diversity may reduce freshness) and propose a unified optimization (e.g., constrained optimization or re-ranking with penalties).
Define offline metrics (e.g., diversity, freshness, cap violation rate) and online A/B tests (e.g., CTR, session length, retention). Monitor for unintended consequences and iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a two-stage setup: candidate retrieval then a reranker.
Start by clarifying the requirements: define the ranking objective, latency SLA, scale, and available data. Then walk through the end-to-end pipeline: data collection, feature engineering, model training, and online serving with low latency. Emphasize trade-offs between model complexity and latency, and how you would monitor and iterate.
Pro tip: Focus on the feature store and caching strategy—these are critical for low-latency serving and often overlooked. Also, mention how you would handle cold-start and ensure consistency between offline and online features.
Ask about the business goal (e.g., increase user engagement), latency SLA (e.g., <100ms), scale (QPS, number of users/items), and available data (user interactions, item metadata).
Outline data ingestion, feature engineering (user, item, context features), and model training (e.g., gradient boosted trees or neural networks). Include offline evaluation and A/B testing.
Describe how to serve features and model predictions in real-time: use a feature store for low-latency feature retrieval, cache precomputed features, and deploy the model as a microservice with efficient inference (e.g., ONNX, TensorFlow Serving).
Discuss techniques like model quantization, pruning, batching, and asynchronous feature fetching. Consider precomputing rankings for common queries and using a two-stage ranking (candidate generation + ranking).
Set up monitoring for latency, throughput, and model performance (e.g., click-through rate). Plan for retraining, A/B testing, and handling feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining the importance of user-level randomization and consistent hashing to ensure each user sees a consistent ranking variant across sessions. Then outline a safe rollout strategy using staged deployment, guardrail metrics, and automated rollback. Finally, discuss how to measure the experiment's impact on feed ranking while accounting for network effects and long-term effects.
Pro tip: Emphasize the need for a pre-experiment power analysis and a clear definition of guardrail metrics to detect unintended consequences early. Also, mention that you would monitor for novelty effects and use holdout groups for long-term measurement.
Clearly define the primary metric (e.g., engagement, CTR) and guardrail metrics (e.g., user satisfaction, latency). Ensure alignment with business objectives.
Use user-level randomization with consistent hashing to assign users to variants. Ensure the same user sees the same variant across devices and sessions.
Start with a small percentage of traffic (e.g., 1-5%) and gradually increase. Set up automated monitoring and rollback triggers based on guardrail metrics.
After sufficient sample size, analyze results using appropriate statistical methods. Check for heterogeneous treatment effects and long-term impact.
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 data flows, then address each concern (GDPR, replication, abuse, outages) with a balanced view of trade-offs between consistency, availability, and cost. Emphasize a data science perspective: how these factors affect model training, feature freshness, and evaluation, and propose monitoring and fallback strategies.
Pro tip: Frame GDPR deletion as a data lineage and provenance challenge—show you understand that deleting a user's data must cascade through derived datasets, features, and models, which is often overlooked. Also, highlight that abuse controls can introduce bias into training data, so you need to monitor for feedback loops.
Ask about data volume, latency requirements, regional regulations, and SLAs to scope the problem. Identify which data is personal vs. derived, and what consistency guarantees are needed.
Propose a deletion pipeline that propagates through all replicas and derived datasets (features, models) using techniques like tombstoning, crypto-shredding, or periodic re-training. Discuss trade-offs between immediate deletion and eventual consistency.
Choose a replication strategy (e.g., active-active, active-passive) based on consistency needs. For abuse/spam, describe a layered approach: rate limiting, anomaly detection, and manual review, and how to integrate with replication (e.g., region-specific rules).
Design for graceful degradation: fallback to stale data, queue writes for later, or route to healthy regions. Discuss how to detect and recover from partial outages, and the impact on model training and serving.
Recap key trade-offs (consistency vs. availability, cost vs. compliance) and propose monitoring for deletion SLAs, replication lag, abuse rates, and outage recovery. Tie back to data science implications like model drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.