How the interviewer opens it
“Design a system that predicts the probability of a purchase or another conversion for an advertisement. The system must return predictions for live ad requests.”
The prompt is vague on purpose. The first minutes are for you. Reduce the prompt to the core features and agree on the numbers before you draw.
What to clarify first
- Prediction target
- Traffic
- Outcome rate and read:write mix
- Purchase delay
- Attribution and missing links
- Features and privacy
- Freshness and retention
- Latency and availability
- Event delivery and finality
- Scope limits
The interviewer has the answers. In a SysRound round, Alex gives these answers, as a real interviewer does.
Scope of a strong answer
Core requirements
- Define the conversion target and create labels from ad and purchase events
- Train and evaluate a model that returns conversion probabilities
- Return probabilities for live ad requests
- Handle delayed events and incomplete purchase links
Usually out of scope
- Auction design and bid selection
- Payments and conversion fraud detection
- Multi-touch attribution
- Estimates of purchases caused by an ad
What interviewers ask about
- What the probability means
- A purchase can follow many ads. Missing links vary by user group, so reported purchases do not reveal every purchase.
- When labels become final
- Future purchases and late exposures can change credit. A model can mistake incomplete labels for low purchase probability.
- Probability accuracy
- Most impressions have no credited purchase. Ad bids need accurate probabilities, not only a useful order of ads.
- Scores for live ads
- The service has 20 ms to return a probability. Feature delays and ad selection can change the data that the model sees.
Common mistakes
- Candidates define a purchase probability without a time window or an attribution rule.
- Candidates finalize labels without complete impression, click, and purchase data.
- Candidates use accuracy to judge data with only 0.1% positive labels.
- Candidates sample negatives without a correction for probability estimates.
- Candidates treat the absence of an attributed purchase as proof that no purchase occurred.
- Candidates use feature values that the score service did not have during inference.
The bar by level
- Senior (L5/E5)
- Calculates scale and label maturity across all event sources. Uses exact score inputs for training. Designs probability checks, bounded latency, compatible releases, and label repair. Evaluates incomplete attribution by cohort.
- Staff (L6+/E6+)
- States which probabilities the available data can identify. Separates coverage changes, purchase delays, and ad selection feedback. Defines release evidence and revision policy. Justifies privacy limits, sparse-cohort conclusions, and cost choices.
Sources · 1 reference
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SysRound · 1 record
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Design an ad conversion prediction system
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Source snapshot: Nov 12, 2026
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