Strategy question
How do you know if the user story you created is ready for execution?
- Salesforce
- Strategy
- Easy
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Whether the candidate understands the practical criteria (like INVEST) that make a user story actionable for an engineering team.
How to approach it
- Clarify what 'ready' means: the team can start work without needing major clarifying questions mid-sprint.
- Check the story has a clear, specific user and goal, not a vague catch-all requirement covering multiple use cases.
- Confirm acceptance criteria are explicit and testable, so both engineering and QA know exactly what 'done' looks like.
- Confirm dependencies are identified, such as needed designs, APIs, or other teams' work, and that they're either resolved or clearly flagged.
- Confirm the story is appropriately sized for a sprint, breaking it down further if it's too large to estimate confidently.
What a strong answer includes
- References concrete readiness criteria, like the INVEST framework (independent, negotiable, valuable, estimable, small, testable), rather than a vague gut check.
- Distinguishes 'ready to estimate' from 'ready to start', since some stories need estimation before final refinement.
- Notes that a story review with the whole team, not just the PM, is the real signal of readiness.
Common mistakes
- Relying only on personal judgment without a shared, explicit definition of ready with the team.
- Ignoring dependency and sizing checks, leading to stories that stall mid-sprint.
Likely follow-up questions
- What would you do if engineering disagrees a story is ready but you think it is?
- How do you handle a story that seems ready but turns out to be much bigger once started?
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More questions from Salesforce
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