Product design question
You're asked to deliver an enterprise GenAI application on Scale’s platform in 10 weeks for a customer with ambiguous requirements, strict security/compliance review, and multiple stakeholder groups. How would you scope the v1, convert discovery into clear requirements, run testing and pilot rollout, and decide what to cut versus what must ship for launch?
- Scale AI
- Product design
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests scoping and delivering an enterprise application under a hard deadline with ambiguous requirements and compliance review, and making clear cut-versus-ship decisions.
How to approach it
- Convert ambiguity into requirements fast: run structured discovery sessions in week one with each stakeholder group to extract the top three must-have capabilities and explicit non-goals.
- Scope v1 around the narrowest capability set that delivers a demonstrable outcome, deferring anything that isn't core to the primary use case, and write these down as an explicit scope document signed off by the stakeholders.
- Front-load security and compliance review, since it's a stated hard constraint, by engaging that team in week one or two rather than treating it as a late-stage gate that could blow the 10-week timeline.
- Build in a testing and pilot window before the deadline, not at it, for example weeks 7 to 9 for internal testing and a limited pilot, leaving week 10 for fixes, not first contact with real users.
- Decide what to cut using the signed-off scope document as the reference: anything not in the must-have list gets cut first when time is short, not negotiated fresh under deadline pressure.
- Communicate cuts early and often to stakeholders as trade-offs made visible, rather than surprising them with a reduced scope at delivery.
What a strong answer includes
- Front-loads compliance review instead of treating it as a late gate, since a 10-week deadline can't absorb a late-stage security blocker.
- Locks a signed-off scope document early, so cut decisions under time pressure reference an agreed baseline instead of being relitigated.
- Reserves real calendar time for testing and pilot before the deadline, not squeezed into the final days.
- Proposes ongoing, proactive communication about cuts, avoiding a last-minute scope surprise for stakeholders.
Common mistakes
- Treating security and compliance review as a final step instead of engaging it from week one.
- Leaving testing and piloting until the very end, with no buffer if issues are found.
- Letting scope stay ambiguous through most of the project instead of locking a signed-off document early.
Likely follow-up questions
- What would you do if compliance review surfaces a blocking issue in week 6?
- How would you handle a stakeholder who disputes a cut after scope was signed off?
More product design questions
- A ministry outside the U.S. asks Scale to build a bespoke GenAI application on top of its proprietary data, but end users cannot clearly explain where the workflow is breaking today. How would you run the first client workshops to uncover the real job-to-be-done, select the highest-value use case, define an MVP, and align the client with Scale’s engineering, MLE, and ops teams on scope?Scale AI · Product design · Hard
- Scale forward deploys to understand real workflows before building. If future end-users in a government agency have different needs from the senior sponsor who is funding the project, how would you gather the right feedback, separate core pain points from feature requests, and turn that into a prioritized roadmap for the first release?Scale AI · Product design · Medium
- A ministry outside the U.S. has several candidate workflows for a bespoke AI solution, but its leadership team is not aligned on which problem is highest priority. How would you run discovery and design workshops to identify the best workflow to target, define a measurable success outcome, and decide whether Scale should build an AI application on top of existing models or invest in a custom LLM?Scale AI · Product design · Hard
- A prospective customer wants an agentic or RL data solution but can only describe the desired outcome, not the tasks, feedback signals, or delivery constraints. How would you run discovery, separate must-have from nice-to-have requirements, and turn the conversation into a concrete plan for product, operations, and next customer validation?Scale AI · Product design · Medium
- You need to ship the first agentic workflow product for defense analysts on a controlled network where internet access, model updates, and human review are tightly constrained. What is the MVP, which user/job would you target first, and what tradeoffs would you make among agent autonomy, user experience speed, and security/risk controls?Scale AI · Product design · Hard
- A defense customer asks for "an AI assistant for analysts" but cannot clearly describe the day-to-day workflow or failure modes. How would you work with engineers and ML teammates to turn that vague request into a concrete v1 product, including the user task you would target, the human-in-the-loop design, and what you would explicitly leave out?Scale AI · Product design · Hard
More questions from Scale AI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop