Behavioral question

Tell me about a time when you had to deep dive and dig data to solve a problem.

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

Amazon's 'Dive Deep' leadership principle: going beyond a surface metric to find a specific, non-obvious root cause.

Situation: pick a real case where a top-line metric looked off or a problem was reported.
Task: state the ambiguity, the initial data didn't explain the issue.
Action: describe the specific deep-dive steps, for example segmenting by cohort, checking raw logs, or querying at the event level.
Result: state what the deep dive found, often a specific, non-obvious cause, and the resulting fix or decision.
Quantify impact where possible, the size of the affected segment or revenue or users involved.
Reflection: note what tooling or habit now helps catch this kind of issue earlier.

Shows genuine analytical depth, specific segmentation or query steps, not just 'I looked at the dashboard more.'
The root cause found is non-obvious and specific, for example a bug affecting only one browser or region.
Quantifies the before-and-after impact of the fix.

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