Describe a decision with incomplete information
Overview
This question evaluates decision-making under uncertainty, covering competencies such as judgment, risk assessment, trade-off analysis, assumption management, and the use of limited data to guide operational choices in a machine learning engineering context.
Amazon
Jul 31, 2025, 12:00 AM
Machine Learning Engineer
Onsite
Behavioral & Leadership
0
0
Behavioral: Decision-Making Without Complete Information (Machine Learning Engineer)
Provide a specific example of a time you made an important decision despite incomplete information. Structure your answer so a first-time reader understands the context and your reasoning.
Prompt
Address the following points:
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Context
- What was the team, product, and goal? What constraints existed (time, compute, data quality/latency)?
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Options Considered
- List at least two viable alternatives and the status quo (do nothing or delay).
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Assumptions and Risks
- What did you believe to be true? What could go wrong (customer impact, latency/SLO, revenue, fairness, compliance, on-call risk)?
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Just-Enough Data
- How did you quickly gather evidence (log replay, A/B canary, offline metrics, back-of-the-envelope estimates) to reduce uncertainty?
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Decision and Rationale
- Which option did you choose and why? Was it reversible? What guardrails did you set?
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Outcome
- What happened (metrics, customer impact, operational results)? Be specific.
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Retrospective
- What would you do differently? How did you mitigate downsides and institutionalize the learning?
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