# Design an ad click-through rate prediction system: system design interview question

> A click probability service must combine fresh features, durable prediction records, delayed labels, and calibrated models within a strict response deadline.

Source: https://sysround.com/questions/design-ad-click-through-rate-prediction-system
Updated: 2026-11-05


Difficulty: hard. Levels: Senior, Staff+.

## How the interviewer opens it

"Design a service that predicts the probability that a user will click a candidate advertisement. The service must return calibrated probabilities at low latency and use fresh user, ad, and context features."

## What to clarify first

- Service boundary
- Scale
- Read and write traffic
- Latency and availability
- Feature freshness and consistency
- Click labels
- Prediction quality
- Model freshness and new entities
- Durability and retention
- Identity and privacy

## Scope of a strong answer

Core requirements:

- Return a click probability for each supplied candidate ad.
- Use recent user, ad, and context features.
- Record each prediction and join actual displays with later clicks.
- Train, assess, and release calibrated models.

Usually out of scope:

- Select candidate ads.
- Run auctions or manage ad budgets.
- Predict purchases or other conversions.
- Operate across multiple regions.

## What interviewers probe

### Feature values at request time

Features change between prediction and training. Stale caches and fallback paths can make the actual prediction differ from any later reconstruction.

### Impressions, clicks, and training examples

Most impressions have no click. Delayed events make recent negatives uncertain, and candidate ads without display have no observed outcome.

### Probability quality and model changes

A model can rank ads well but return wrong probabilities. Aggregate quality can hide errors in fallback traffic or other important slices.

### Prediction latency under load

Each request needs 20 probabilities and a durable record within 20 ms. Feature access and model execution share this limited budget.

## Common mistakes

- Treats the problem as model selection and omits the event, feature, and release paths.
- Stores impression IDs but omits request context, served probabilities, or effective versions from durable prediction records.
- Uses future feature values in historical training examples.
- Labels unshown candidates or immature impressions as negative examples.
- Uses accuracy or AUC as proof that probabilities are calibrated.
- Ignores fallback traffic, negative sampling corrections, or durable record costs.

## The bar by level

- **Senior:** Defines exact prediction records and historical examples. Handles delayed labels and calibration. Budgets feature access, model execution, and durable commits within deadlines.
- **Staff+:** Defines durability and retention across every prediction path. Addresses display bias and fallback calibration. Plans zone failure capacity and evidence-based release gates.

Practice this question with an AI interviewer: https://sysround.com/practice/new?problem=ad-click-prediction


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SysRound (https://sysround.com) is an AI practice tool for system design interviews. It has a voice or typed AI interviewer and a shared whiteboard. At the end, you get feedback on each skill and a model answer. The AI feedback can be wrong.