The PM loop as it is actually run: product sense, analytical, execution, and strategy rounds, each with its own scoring rubric.
14 published across 7 topics.
Product management fundamentals60 short answers on one page, for revising rather than studying.Improve-this-product and design-for-this-user questions, and structuring an answer around a real user problem.
Argue from the cost the feature imposes on everyone who does not use it — surface area, decision load, engineering drag — then quantify who depends on it, find where their need lands afterwards, and stage the removal so you learn before you are committed.
It is a structure test, not a creativity test: state the product's goal and how it earns money, name one segment and one unmet need, generate several options before choosing, then commit to a metric and the risk that would prove you wrong.
RICE, Kano, cost of delay, and defending a roadmap cut to an unhappy stakeholder.
Convert the migration into the units the roadmap already uses by pricing its carrying cost and the cost of delaying it, then rank it on the same scale as features rather than arguing for it as a special category that deserves exemption.
A framework's job is to make the trade-off legible, not to produce a verdict: ring-fence hard-dated and reliability work first, score the discretionary remainder on one agreed model, and communicate each no as what it displaces plus the condition that would reverse it.
Choosing a north-star metric, diagnosing a drop, guardrail metrics, and estimation questions.
Ratios improve when the denominator shrinks and averages improve when the mix shifts, so a rate can rise while every underlying cohort worsens; you catch it by pairing every rate with its absolute counts, decomposing by segment, and defining guardrails before launch.
Rule out instrumentation before behaviour, then segment along the dimensions that isolate a cause — platform, geography, cohort, and acquisition channel — to distinguish a broad drop from a concentrated one, since those have entirely different explanations.
Opportunity solution trees, interview technique, assumption testing, and avoiding confirmation bias.
Establish whether the test could ever have detected the effect you cared about, because an underpowered null tells you nothing; then read the confidence interval rather than the verdict and decide whether to extend, test a larger change, or accept that the assumption was wrong.
You look for evidence of the behaviour, not enthusiasm for your idea: interviews about past specific episodes rather than hypothetical futures, triangulated against what usage data, support tickets and lost deals show people already do.
Scope cuts under a deadline, launch readiness, working with engineering, and handling a slipped date.
Find out what the date is being used for, then commit to something you can actually control — a timeboxed discovery end, a date for the date, or a narrow first slice — and never issue a single-point estimate for scope that is still undefined.
Find out what the six weeks is made of before negotiating anything, then choose between cutting scope, splitting the release, launching to a limited cohort and moving the date — and tell everyone who depends on the date the same day you know.
Segmentation, competitive positioning, build-buy-partner, and pricing and packaging reasoning.
Absence of requests is a demand signal you have to explain before you price anything; once you know whether the need is latent or absent, choose the packaging first — core, tier upgrade, or add-on — and price against the buyer's alternative rather than your build cost.
Establish whether anything changed for your customers before changing your plan, then choose deliberately between accelerating, differentiating, ignoring it and reframing the category — and treat the competitor's launch as evidence about the market rather than an instruction.
Full cases worked end to end with the interviewer's evaluation criteria stated up front.
Equal revenue hides unequal quality of revenue, so compare the two on retention, expansion, cost to serve, cost to acquire and headroom, then pick the one whose limiting constraint you can actually change in a year and name the condition that would reverse the choice.
Work top-down from households through cooking behaviour, planning behaviour and willingness to pay, price it, then sanity-check the implied penetration — the honest answer lands near ten million pounds a year, which is the finding that decides the recommendation.