The 100ms constraint is the first thing that should shape every decision you make, and I didn't anchor on it hard enough early on.
Start by clarifying requirements and scale, then walk through the high-level architecture from ad creation to serving and reporting. Focus on the critical path for ad serving (targeting, auction, pacing) and explain how you meet the 100ms latency budget with caching, efficient data structures, and asynchronous processing. Finally, discuss trade-offs and how you would handle scale and failures.
Pro tip: Emphasize the separation of the write path (campaign management, budget updates) from the read path (ad serving) and how you use eventual consistency for reporting without impacting latency. Also, mention that you would use a fast in-memory store for real-time budget and pacing counters.
Ask questions to understand expected QPS, number of active campaigns, targeting dimensions, latency SLA, and reporting freshness. This shapes the design and trade-offs.
Outline the main components: campaign management service, ad serving service, real-time auction, budget/pacing service, frequency capping, and reporting pipeline. Explain data flow from ad creation to serving and logging.
Detail how an ad request is processed within 100ms: user targeting, candidate retrieval, auction, budget/pacing checks, frequency capping, and ad selection. Discuss caching, indexing, and parallel processing.
Explain how budgets are tracked in real-time (e.g., using Redis or a distributed counter), how pacing algorithms work (e.g., probabilistic throttling), and how frequency capping is enforced with low-latency storage.
Describe the near-real-time reporting pipeline (e.g., stream processing with Kafka and Flink) and discuss trade-offs between consistency, latency, and cost. Also cover failure handling and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and requirements of the ad request/response API, then walk through the request and response structures, and finally define the core data models for campaigns, bids, and user targeting. Emphasize scalability, low latency, and data consistency throughout.
Pro tip: Highlight trade-offs between real-time bidding and budget pacing, and discuss how you would handle high throughput with caching and async processing. Mentioning idempotency and error handling shows production maturity.
Ask about expected QPS, latency SLAs, data consistency needs, and whether the API is for real-time bidding or batch processing. This ensures your design aligns with business goals.
Define the request (e.g., user context, ad slot info, targeting parameters) and response (e.g., selected ad, bid price, tracking URLs) with clear field names and types. Consider using REST or gRPC and specify error codes.
Outline schemas for Campaign (id, budget, schedule, status), Bid (campaign_id, amount, targeting criteria), and User Targeting (demographics, interests, behavior). Discuss relationships and indexing.
Explain how you would handle high throughput with caching, sharding, and async processing. Discuss trade-offs between consistency and latency, and how to ensure idempotency.
Mention logging, metrics, and alerting for the API. Talk about versioning, backward compatibility, and how to evolve the data models over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and requirements of the ad delivery system, then present a high-level architecture that covers all mentioned components, explaining how they interact. Focus on data flow and trade-offs, and be prepared to dive deeper into any component if asked.
Pro tip: Emphasize the importance of low-latency and high-throughput in ad serving, and discuss how you would monitor and optimize each component. Show awareness of failure modes and how to design for resilience.
Ask clarifying questions to understand scale, latency requirements, and key constraints. Confirm whether this is for real-time bidding, ad selection, or both.
Sketch the main components and their interactions: edge gateways handle incoming requests, ad selector chooses ads, feature store provides features, model service runs ML models, auctioneer runs the auction, throttling and pacing service controls ad delivery, caching improves performance, and logging pipeline captures data.
Explain the role of each component in detail, focusing on how they work together to meet latency and throughput goals. Discuss trade-offs such as consistency vs. availability in the feature store, or model complexity vs. inference speed.
Describe the end-to-end flow: from user request hitting edge gateway, to ad selection, feature retrieval, model scoring, auction, throttling, and finally logging. Highlight where caching is used and how data is passed between services.
Discuss how the system scales horizontally, handles failures (e.g., fallbacks if model service is down), and how logging pipeline supports monitoring and debugging. Mention any trade-offs made for scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Second-price auction, said it immediately.
Start by clarifying the context—what is being auctioned (e.g., ad slots, API access) and the goals (revenue, efficiency, fairness). Then compare mechanisms like first-price, second-price, and VCG, and recommend one with justification. Finally, explain how your choice shapes advertiser incentives (e.g., truthful bidding, bid shading).
Pro tip: Acknowledge that no auction is perfect; discuss trade-offs and suggest hybrid or practical adjustments (e.g., reserve prices, pacing) to mitigate weaknesses. This shows real-world engineering judgment.
Identify what is being auctioned, the number of bidders, and the primary goals (e.g., revenue maximization, allocative efficiency, simplicity). This sets the stage for mechanism choice.
Briefly outline options like first-price, second-price (Vickrey), and VCG, highlighting their theoretical properties (e.g., incentive compatibility, efficiency) and practical challenges.
Choose one mechanism (e.g., second-price) and explain why it aligns with the context and goals, referencing trade-offs such as simplicity vs. optimality.
Explain how the chosen mechanism influences bidding behavior: does it encourage truthful bidding, bid shading, or strategic manipulation? Discuss implications for revenue and efficiency.
Mention real-world adjustments (e.g., reserve prices, pacing, fraud detection) and how they interact with incentives, showing awareness of engineering constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about prior-based initialization using similar campaigns, content-based features from the creative itself, and a short exploration phase with UCB-style allocation.
Start by framing the cold start problem as a classic exploration-exploitation trade-off, then propose a multi-pronged strategy that combines content-based signals, contextual bandits, and transfer learning. Emphasize how you would measure success and iterate, while being mindful of the unique constraints of an AI-driven ads system at OpenAI.
Pro tip: Show that you understand the business impact: cold start isn't just a technical problem; it directly affects advertiser ROI and platform revenue. Mention that you'd prioritize solutions that quickly gather signal without sacrificing user experience or ad quality.
Clarify what 'cold start' means in this context: new advertisers with no campaign history, new creatives with no performance data, and possibly new ad formats. Discuss constraints like latency, privacy, and the need for real-time bidding.
Use features from the ad creative (text, image, video embeddings) and advertiser metadata (industry, target audience) to make initial predictions. For example, use a pre-trained model to embed creatives and match with similar historical ads.
Implement a multi-armed bandit or Thompson sampling approach to allocate a small portion of traffic to new ads, balancing exploration and exploitation. Use contextual bandits to personalize exploration based on user and ad features.
Train a model on existing advertisers/creatives and fine-tune for new ones. Meta-learning can help the model quickly adapt to new tasks with few examples. Consider using OpenAI's models for few-shot learning.
Define metrics like click-through rate, conversion rate, and revenue lift. Set up A/B tests to compare strategies. Implement guardrails to prevent poor user experience, such as capping exploration traffic and using quality scores.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Trickier than a standard product A/B test because budget pacing and auction competition can leak between experiment arms.
Start by framing the core challenge: A/B tests in ad auctions are not independent because variants compete for the same budget and influence auction outcomes. Then propose a design that isolates treatment effects using techniques like budget-split randomization, switchback experiments, or counterfactual logging, and discuss how to measure incremental impact despite interference.
Pro tip: Emphasize that in auction systems, the 'stable unit treatment value assumption' (SUTVA) is often violated, so you need to either design around interference or explicitly model it—mentioning this shows deep understanding.
Identify what you're testing (e.g., ranking model, bid strategy) and the constraints: shared budget, auction dynamics, and potential interference between variants.
Decide whether to randomize at user, session, or auction level, considering that budget and auction competition can cause spillover effects between units.
Use techniques like budget-split (separate budgets per variant), switchback experiments (time-based alternation), or cluster randomization to reduce interference.
Account for interference in analysis using methods like difference-in-differences, instrumental variables, or counterfactual logging to estimate incremental lift.
Run A/A tests, check for budget depletion, and monitor auction metrics to ensure the experiment is valid and results are actionable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered consent signals at the request layer, data minimization in the feature store, and right-to-erasure propagation through the logging pipeline.
Start by acknowledging that privacy and compliance are not afterthoughts but core design principles, especially at a company like OpenAI. Then, walk through a structured approach that covers data minimization, user consent, access controls, and retention policies, while balancing technical feasibility and business needs. Emphasize that you would collaborate with legal and privacy teams to translate requirements into technical specifications.
Pro tip: Demonstrate awareness that GDPR and CCPA have nuanced differences (e.g., GDPR's lawful basis vs. CCPA's opt-out for sale of data) and that compliance is an ongoing process, not a one-time checkbox. Mention that you would design for privacy by default and by design, and consider data localization and cross-border transfer mechanisms.
Determine what user data is collected, how it's used for targeting, and which data is subject to GDPR/CCPA. Classify data by sensitivity and retention requirements.
Incorporate data minimization, purpose limitation, and consent mechanisms into the system architecture. Ensure user targeting respects opt-in/opt-out preferences and anonymizes data where possible.
Design role-based access controls and automated retention schedules. Implement deletion and anonymization workflows to honor user requests (e.g., right to be forgotten).
Set up logging, auditing, and regular reviews to ensure ongoing compliance. Use tools to track data flows and detect policy violations.
Work closely with legal teams to interpret regulations and adapt as laws evolve. Build feedback loops to update technical measures accordingly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Did the math out loud: assumed a few billion ad requests per day, worked backwards to QPS, then estimated feature store read throughput and log ingestion volume.
Start by clarifying the system's scope and key assumptions (e.g., user base, request types, data size). Then walk through a structured back-of-the-envelope calculation for request volume, storage, and infrastructure, using round numbers and stating your reasoning at each step. Conclude by discussing how you would validate and refine these estimates.
Pro tip: Always state your assumptions explicitly and use powers of 10 to simplify calculations. This shows you can think quantitatively and communicate clearly, which is crucial for system design at scale.
Ask clarifying questions to understand the system's purpose, expected user base, request patterns, and data retention requirements. State any assumptions you make.
Calculate the number of requests per second (RPS) by estimating daily active users, average requests per user per day, and peak traffic multipliers.
Determine data size per request or user action, multiply by volume, and factor in replication, backups, and retention policies to get total storage needs.
Translate request volume and storage into compute, memory, and network requirements. Consider server capacity, database sharding, and caching layers.
Discuss how you would validate estimates with benchmarks, load testing, or monitoring, and how you would adjust as real usage data comes in.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.