Strategy question
A frontier-model customer needs a first delivery of image and video training data in 6 weeks, but current throughput and QA capacity suggest you will miss either quality or timeline. How would you structure the program across operations, engineering, research, and go-to-market; what milestones and risk signals would you track; and how would you decide whether to change scope, spec strictness, or staffing to protect the customer commitment?
- Scale AI
- Strategy
- Medium
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What this question tests
Tests program management under a hard deadline and capacity constraint, and the judgment to protect a customer commitment without silently sacrificing quality.
How to approach it
- Get an honest current throughput and QA capacity estimate from operations before making any commitment adjustments.
- Break the 6 week timeline into milestones, data sourcing, annotation, QA, and delivery, each with its own risk buffer.
- Track leading risk signals weekly, such as annotation throughput versus plan and QA rejection rate, not just the final delivery date.
- If a gap emerges, decide explicitly whether to flex scope, for example fewer categories, or spec strictness, slightly relaxed QA bar with disclosed limitations, rather than silently doing both.
- Escalate early to the customer with options rather than surprising them at the deadline, since transparency preserves trust even if timeline slips.
- Bring in engineering to automate any QA bottleneck if it is the binding constraint, rather than just adding more headcount.
What a strong answer includes
- Tracks leading indicators weekly, not just the final deadline, so risk is caught early enough to act on.
- Proposes a specific, named tradeoff, scope versus spec strictness versus staffing, rather than hoping the team just works harder.
- Prioritizes early, transparent escalation to the customer with options over a last minute surprise, which protects the relationship even under a miss.
Common mistakes
- Waiting until close to the deadline to surface risk instead of tracking leading indicators from week one.
- Quietly relaxing quality standards to hit the deadline without disclosing the tradeoff to the customer.
Likely follow-up questions
- How would you decide between adding staff and relaxing the spec if both are on the table?
- What would you tell the customer if you realized in week 2 that 6 weeks was not feasible at all?
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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 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop