Google Ads

Advertiser creates a campaign. Every search query or page-view triggers an ad auction in under 100 ms. The winner's ad is shown, the click is tracked, and the advertiser is billed on a CPC basis. Scale: 10M+ auctions per second. The hard parts: a second-price auction with quality scoring so the best ad wins (not just the richest bidder), budget pacing that spends the daily budget evenly instead of blowing it in one hour, and click-fraud detection that filters bots and click farms before billing advertisers. Google, Meta, Amazon — same pattern, different inventory.

Time budget: 45 min whiteboard. Draw architecture, estimate numbers, discuss tradeoffs.

• +2 Lead with the auction model. "Second-price, Ad Rank = bid x quality score." This shows you understand the economic incentive structure, not just the tech.
• +2 Quality score is the key insight. Without it, ads devolve into a pure spending war. Quality score aligns platform revenue with user experience — interviewers love this.
• +2 Budget pacing is non-obvious. Most candidates forget it. "Probabilistic throttling to spread daily budget evenly" — this is a production detail that separates senior from junior.
• +2 Click fraud is a billing integrity problem. Frame it as: "we cannot charge advertisers for bot clicks." Two layers: real-time filtering + batch ML + refunds.
• +2 Distinguish search ads from display RTB. Search = server-side auction among own campaigns. Display = fan-out to external DSPs in real-time. Same auction principles, different latency profiles.

Self-score: tally the points you would have mentioned unprompted. 7+ is interview-ready on this problem.

• First-price auction — winner pays exactly their bid
• No quality score — highest bidder always wins
• No budget pacing — serve ads until daily budget is gone