Metrics question

You notice a spike in the number of user-uploaded videos on your platform in June. What do you think is the cause, and how would you test it?

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

Tests hypothesis generation and testing for a sudden content-supply spike, connecting cause diagnosis to a concrete verification plan.

Clarify scope: confirm whether the spike is in upload count, total minutes uploaded, or both, and how large the spike is.
Generate seasonal hypotheses: June often includes graduations, the start of summer break, and Pride Month content, all plausible drivers.
Generate product hypotheses: a recent feature launch, an easier upload flow, a new editing tool, or a raised upload size limit shipped around that time.
Generate external hypotheses: a viral trend or challenge format specific to June driving a wave of similar videos.
Propose tests: compare the spike against the same month in prior years to isolate a recurring pattern, and segment new uploads by category or hashtag to see if one theme dominates.
Define the follow-up action: if seasonal, prepare capacity and moderation for a recurring pattern; if feature-driven, consider promoting that upload flow further.

Generates hypotheses across three distinct categories, seasonal, product, and external trend, rather than guessing one cause.
Proposes a concrete year-over-year comparison to separate a recurring seasonal pattern from a genuine anomaly.
Names specific, plausible June events, graduations, Pride Month, summer break, rather than vague seasonality.
Ties the diagnosis to an actionable next step depending on which cause is confirmed.

Treating seasonality as a black-box explanation without checking it against prior years' data.
Not segmenting the spike by content category, missing that one theme could be driving most of it.

Chapters of the AI PM course that teach what this question tests.