How the interviewer opens it
“Design a service that chooses a recommendation or advertisement for each request. The service must learn from user responses and respect safety limits.”
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
- Decision inputs
- Scale and API mix
- Reward
- Feedback delay
- Latency and policy updates
- Hard restrictions and retries
- Exploration and policy loss
- Consistency and availability
- Durability and retention
- Scope and privacy
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
- Choose one eligible item for each request
- Learn from confirmed impressions and attributed conversions
- Limit exploration and enforce current item restrictions
- Compare policy results against a concurrent baseline
Usually out of scope
- Find candidate items
- Run an advertisement auction
- Resolve user identity
- Build the conversion attribution system
- Enforce restrictions when the caller later displays the item
What interviewers ask about
- Choices and retries
- Each request has different candidates and context. Retries must preserve the original choice unless current restrictions forbid it.
- Late responses and model changes
- Recent impressions lack complete rewards. Frequent model refreshes can create many comparisons before any comparison has enough mature evidence.
- Limits on live changes
- Hard restrictions require immediate authority. Delayed conversion evidence cannot prevent all reward loss before a policy stop.
- Evidence for policy changes
- The policy controls which outcomes become visible. Rare choices and repeated tests can make weak evidence appear decisive.
Common mistakes
- Describes a bandit algorithm but omits the decision service, feedback path, and release process.
- Treats missing or recent feedback as a negative reward.
- Replays a forbidden item because the original decision already exists.
- Exposes every refreshed model before earlier trials have mature evidence.
- Confuses a low exploration rate with a safe policy release.
- Claims improvement without a fair comparison or valid error control across repeated tests and releases.
The bar by level
- Senior (L5/E5)
- Defines exact choice probabilities and mature reward cohorts. Separates frequent model refreshes from fixed live trials. Specifies exposure caps, durable records, and failure behavior. Uses a valid loss test.
- Staff (L6+/E6+)
- Defines the authorization boundary for hard restrictions. Controls statistical error across releases and repeated tests. Challenges dependence and action-support assumptions. Quantifies delayed harm exposure and explains recovery without changing trial meaning.
Sources · 1 reference
These records explain where the question appears. A company tag or candidate report does not guarantee a future interview question.
SysRound · 1 record
No company association
Design a contextual bandit system
Reported date not specified
Source snapshot: Nov 12, 2026
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