Behavioral question
You are six weeks into a classified deployment with incomplete requirements; senior military users say engineers 'do not understand the mission,' while engineers say user requests change every week. How would you rebuild trust on both sides, create enough operating structure to make decisions, and communicate progress upward without overpromising?
- Scale AI
- Behavioral
- Medium
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What this question tests
Whether you can rebuild trust and create operating structure in a technically and politically difficult deployment with genuinely ambiguous requirements.
How to approach it
- Acknowledge both sides' complaints as partially valid: engineers cannot build against constantly shifting asks, and users cannot trust a team that seems disconnected from the mission.
- Spend time directly embedded with the end users first, observing their workflow, to build genuine understanding before proposing any process change.
- Introduce a lightweight requirements cadence, for example a short recurring session where users prioritize the top few needs for the next cycle, so change is expected and bounded rather than constant and unbounded.
- Set a clear change-control process: new requests go into a prioritized backlog reviewed on a fixed cadence, rather than landing on engineers ad hoc mid-sprint.
- Communicate progress upward with honest, specific status, for example naming what shipped, what changed, and what remains uncertain, rather than a falsely confident summary.
- Use small, visible wins (a specific feature that addresses a stated mission need) early to rebuild trust with users before tackling larger structural issues.
What a strong answer includes
- Validates both perspectives instead of siding with either engineers or users, showing balanced judgment in a politically sensitive setting.
- Proposes a concrete cadence mechanism (bounded prioritization sessions) that solves the 'requests change every week' complaint without shutting users out.
- Emphasizes direct observation of the mission context as the way to genuinely understand user needs, appropriate for a classified, mission-critical setting.
- Describes upward communication as honest and specific, avoiding the overpromising trap the question explicitly warns against.
Common mistakes
- Siding entirely with either the users or the engineers instead of addressing both root complaints.
- Proposing a heavy process overhaul that will not survive six more weeks of an already-strained relationship.
- Overpromising upward to appear in control, which the question explicitly flags as a risk.
Likely follow-up questions
- How would you handle a request that genuinely cannot wait for the next prioritization cycle?
- What would you tell leadership if trust has not visibly improved after a few weeks?
- How would you balance mission urgency against the discipline of a fixed cadence?
More behavioral questions
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- Walk me through a specific enterprise AI/ML or complex software deployment you owned from signed contract to production. How did you define production readiness up front, what were the top risks across integration, security review, and change management, and how did you decide which blockers required product changes versus account execution fixes?Scale AI · Behavioral · Hard
- Describe a past project where you had to give regular updates to a demanding external stakeholder while the product direction was still changing. How did you decide what to communicate, how did you reset expectations when scope or timelines moved, and how did you keep the internal team aligned?Scale AI · Behavioral · Medium
- Tell me about a time you had multiple cross-functional projects competing for the same people or deadline. How did you decide what to deprioritize, how did you keep stakeholders aligned, and what was the outcome?Scale AI · Behavioral · Medium
- You’re building a team of PMs who will be embedded in complex enterprise and government deployments, yet also need to influence core product direction. How would you design the hiring profile, staffing model, and coaching system for this team so that PMs can both unblock customers in the field and surface high-quality product insights back to the platform teams?Scale AI · Behavioral · Hard
More questions from Scale AI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 13: Lead the room: staff moves, forward-deployed PM, and the portfolio
- Chapter 14: Get the job: the AI PM interview loop