AI & Technical question

How would you reduce the total storage required by Google Photos?

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

Technical product thinking on storage optimization, balancing infrastructure cost savings against user-perceived quality.

Clarify the constraint, are we solving Google's storage cost or the user-facing 'storage full' problem, since solutions differ.
Identify storage cost drivers, redundant near-duplicate photos, high resolution originals rarely viewed at full size, and long-unused backups.
Propose compression strategies, smarter automatic compression for photos rarely re-opened at full resolution, with an opt-out for power users.
Propose deduplication, detecting near-duplicate bursts and screenshots to suggest or auto-remove redundant copies.
Consider a tiered storage approach, moving rarely accessed photos to cheaper cold storage with slightly higher retrieval latency.
Define success metrics, total storage reduction percentage and user complaint or opt-out rate from any quality-affecting change.

Separates deduplication, compression, and tiered storage as three distinct levers, rather than one vague 'compress everything' answer.
Protects user trust by proposing opt-in or reversible compression for quality-sensitive photos, avoiding a repeat of past trust issues around photo compression.
Uses smart deduplication, e.g. flagging burst-mode near-duplicates, as a low-risk, high-value first step.
Sets a dual metric, storage saved and user complaint rate, to catch a fix that saves cost but damages trust.

Proposing blanket compression with no regard for perceived photo quality or user trust.
Ignoring deduplication of near-duplicate images as an easy first win.
Not defining a metric to catch user backlash from a quality-affecting change.

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