The 60-second answer
Clarify product objective, prediction/decision target, latency/throughput constraints, feedback timing, and false-positive/false-negative costs. Define data, labels, leakage boundaries, features, and a simple baseline before selecting complex models.
Build the answer in this order
Clarify product objective, prediction/decision target, latency/throughput constraints, feedback timing, and false-positive/false-negative costs.
Define data, labels, leakage boundaries, features, and a simple baseline before selecting complex models.
Separate candidate generation/modeling, offline evaluation, online experimentation, and serving architecture.
Monitor data quality, drift, slice metrics, latency, cost, and user/business outcomes with rollback.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Explicitly reason about feedback loops, delayed labels, cold start, and how policy/product changes alter the data-generating process.
- Version data, features, models, and decision policy independently for attribution and safe rollback.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Jumping to a model before defining the product contract.
- Listing components without bottlenecks, metrics, or failure handling.
- Ignoring data quality, serving latency, monitoring, and iteration.