Metrics question

TikTok often updates its algorithm. How would you measure its impact on user engagement?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Experimentation rigor: designing a measurement approach for an algorithm change that isolates its effect from noise and seasonality.

Clarify the update type: ranking weight change, new signal added, or a different objective function.
Propose an A/B test with a held out control group on the old algorithm, run long enough to cover a full weekly cycle.
Define primary metrics: session time, return rate, and video completion rate, not just raw watch time which can be gamed.
Add guardrail metrics: content diversity and creator distribution, to catch unintended narrowing of the feed.
Watch for novelty effects by extending the test window and checking if lift persists past the first week.
Confirm with the interviewer whether the update targets engagement or another goal like diversity.

Uses a proper control group rather than before and after comparison, which confounds seasonality with the change.
Names guardrail metrics like content diversity, avoiding a narrow engagement at any cost read.
Explicitly checks for novelty effect by extending the measurement window past the first week.

Comparing metrics before and after launch without a control group, confounding other factors with the change.
Measuring only raw watch time, which autoplay can inflate without reflecting real satisfaction.

How would you detect if the algorithm change is favoring one content category too heavily?
How long would you run the test before making a rollout decision?