Product design question
Sierra is onboarding a large Brazilian enterprise that needs a bilingual (Brazilian Portuguese/English) support agent. How would you gather requirements from executives, support operations, and technical stakeholders, and turn them into a scoped MVP with clear in-scope vs. later capabilities?
- Sierra
- 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 bilingual enterprise requirements gathering and MVP scoping across executive, operations, and technical stakeholders.
How to approach it
- Gather requirements separately from executives, who care about brand risk and ROI, and support operations, who know the actual conversation volume and pain points.
- Gather technical requirements, such as existing systems, data residency, and integration constraints, from IT and engineering stakeholders.
- Scope the MVP around the highest volume Portuguese and English intents shared across both languages, rather than full parity from day one.
- Defer lower volume or highly nuanced intents, like complex complaint handling, to a later phase while covering them with human escalation.
- Set explicit in scope versus later criteria in writing and confirm them with the customer before build starts, to avoid scope creep mid project.
What a strong answer includes
- Separates requirements gathering by stakeholder type instead of one generic discovery call, since executives and ops care about different things.
- Chooses a shared high volume intent set for the MVP instead of promising full bilingual parity from day one.
- Gets explicit written sign off on in scope versus later scope with the customer to prevent mid project disputes.
Common mistakes
- Promising full bilingual feature parity at launch instead of scoping around the highest volume shared intents.
- Skipping technical stakeholder input and discovering integration constraints only after build has started.
Likely follow-up questions
- How would you handle a request to add a low volume but high visibility intent mid build?
- What would you measure in the first 30 days to confirm the bilingual scope was right?
More product design questions
- How would you improve Sierra's AI agents to resolve more customer issues without escalation?Sierra · Product design · Medium
- Design an agent that works seamlessly across chat, voice, email, and SMS.Sierra · Product design · Hard
- If you joined Sierra today, what developer-facing platform capabilities would you put in v1 to help engineers deploy, observe, and iterate on AI agents reliably? Be specific about the first APIs, SDKs, workflows, or internal tools you would build, who they serve, and what criteria would determine whether something belongs in the initial platform versus a later release.Sierra · Product design · Medium
- A large healthcare payer wants Sierra to launch a customer-support agent in 8 weeks. How would you discover requirements, choose the first workflows to automate, and define an MVP that is safe enough for launch but still delivers measurable value?Sierra · Product design · Hard
- Sierra is launching the first version of its Agent SDK for enterprise developers. What would you include in the v1 launch scope, and how would you prioritize among core integration primitives, customization hooks, observability, safety controls, and brand configuration? Be explicit about the tradeoffs you would make to balance fast time-to-value with enterprise requirements.Sierra · Product design · Hard
- Sierra is considering a new SDK capability to help companies create more brand-aligned, human-sounding agents. How would you identify the right opportunity, validate that customers will use it, and decide whether it is worth investing in as a 0→1 product bet?Sierra · Product design · Hard
More questions from Sierra
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