Product design question

How would you improve the Amazon Kindle Direct Publishing?

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

Product improvement thinking for a real self-publishing platform, balancing author discoverability and reader trust.

Clarify: assume the goal is improving the author experience or discoverability, or reader trust in independently published content.
Identify the author's top pain point: discoverability is hard for new or unknown authors buried under a massive self-published catalog.
Identify the reader's pain point: quality variance makes it hard to trust an unknown indie title, a related problem worth naming.
Prioritize discoverability, since author retention depends on earning meaningful sales, and the platform's incentive is a healthy long-tail catalog.
Propose better sub-genre tagging and a 'readers also enjoyed' recommendation tuned for indie titles, plus clearer author-facing sales analytics.
Define success as median sales for new or unranked authors in their first 90 days and category-page click-through to purchase.

Names the well-known, real pain point for indie authors, discoverability in an oversaturated catalog, rather than generic 'improve the platform.'
Distinguishes author-side and reader-side problems explicitly, showing the two-sided nature of the marketplace.
Picks a metric, median new-author sales, that specifically measures whether the long tail is being served, not just top-seller performance.

Proposing changes that only benefit already-successful top authors, ignoring the long-tail discoverability problem core to the platform's value.
Giving no author-facing analytics or tools angle, an important lever for author retention.

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