AI & Technical question

How would you design an algorithm to rank ads in the Google Play Store?

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 technical and product thinking on a ranking system with an inherent conflict between relevance and revenue, a classic ads-ranking trade-off.

Clarify the objective: rank ads in Play Store search/browse results to balance relevance to the user's intent, app quality, and revenue (bid) from advertisers.
Define the input signals: the advertiser's bid, predicted click-through rate (pCTR) for this user and query, predicted post-click quality (install rate, uninstall rate, app rating), and query relevance.
Propose the ranking formula structure: rank by expected value, roughly bid times pCTR times a quality multiplier, similar to standard auction-based ad ranking (as used in search ads).
Address quality control: apps with high uninstall rates or low ratings should get a quality penalty in the multiplier so a high bid cannot buy visibility for a bad app.
Address user trust: clearly label sponsored results and cap the number of ad slots per results page so organic relevance is not overwhelmed.
Define success metrics: ad revenue per search, but guarded against user-harm signals like app uninstall rate within a day and search abandonment rate.

Uses the standard auction-ranking structure (bid times predicted CTR times quality score) rather than inventing an ungrounded formula, showing familiarity with how ad ranking actually works.
Explicitly ties quality score to post-install behavior (uninstalls, ratings), not just click behavior, which prevents rewarding clickbait-y app listings.
Names a concrete guardrail metric, next-day uninstall rate, to catch cases where an ad wins ranking but harms the user experience.
Addresses the user trust dimension (clear sponsored labeling, capped ad density) which interviewers often check for in ads-ranking questions.

Designing a pure relevance-ranking algorithm and forgetting this is specifically an ads system with a revenue objective.
Ignoring quality signals, which would let a high bid dominate rankings for a low-quality or scammy app.
No mention of user trust or ad density limits.

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