Tell Me About a Time You Made a Difficult Decision With Incomplete Information - Bar Raiser Evaluate

Evaluate These Two Answers

"Tell me about a time you made a decision with incomplete data and how you ensured it was the right choice."

SDE 2⏱ 3 minAmazon Bar Raiser. LP evaluated explicitly. Content scored, not delivery.

Score BOTH candidates on Ownership Signal, Action Specificity, and Quantified Impact BEFORE applying the rubric weights.

If you scored Candidate A >40 total, your calibration is biased toward fluency. Bar Raisers ignore delivery and score content only.

Candidate A

During a sprint, my manager suggested I look into this since I had bandwidth. I identified a latency issue affecting checkout times. I collaborated with the team to find root causes and deployed a fix. The latency improved by approximately 20%, reducing checkout delays and improving customer satisfaction. This was part of our ongoing efforts to improve system performance.

Fluent delivery, confident tone - most untrained evaluators score this high

Candidate B

While reviewing system logs, I noticed an unusual spike in checkout latency that wasn’t assigned to my team and had no existing ticket. I weighed the risks and benefits, acted with about 70% of the data available, and decided to investigate proactively. I isolated a race condition causing delays, implemented a fix that reduced latency by 30%, which improved customer checkout success rates by 5%. I monitored outcomes closely and iterated on the solution based on real-time feedback, ensuring the fix was robust and scalable.

35-55 seconds longer - every extra second is signal-dense content

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Score Comparison

Dimension

Weight

Candidate A

Candidate B

structure star

15%

10

14

ownership signal

30%

1

28

action specificity

25%

8

24

quantified impact

20%

6

19

self awareness

10%

0

10

Total

25 No Hire

95 Strong Hire

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Auto-Fail Markers

manager-directed task assignment

"Candidate A - my manager suggested I look into this since I had bandwidth"

Ownership requires self-initiation. Manager-assigned = execution. Score 1 on ownership_signal (weight=30) = No Hire always.

collective language hiding individual contribution

"Candidate A - we found a latency issue"

Using 'we' obscures candidate’s individual ownership and decision-making. Lowers ownership_signal score significantly.

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Bar Raiser Notes

Ownership weak - manager-directed; collective language obscures individual role; no quantification of impact; no self-awareness or learning described; No Hire.

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Fix-It Challenge

ownership phrasing

Before"my manager suggested I look into this since I had bandwidth"

→

After"I noticed the issue during a routine review and decided to investigate proactively without being asked"

Demonstrates self-initiation and ownership rather than manager assignment

individual contribution clarity

Before"we found a latency issue"

→

After"I identified a latency issue"

Clarifies candidate’s personal role and decision-making responsibility

quantify impact

Before"The latency improved by approximately 20%, reducing checkout delays and improving customer satisfaction"

→

After"The latency improved by approximately 20%, reducing checkout delays and improving customer satisfaction"

Quantifies impact to show business value and result of candidate’s actions

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Coaching Notes

• At Amazon, Are Right a Lot means candidates must demonstrate strong ownership by initiating investigations themselves rather than acting on manager direction; phrases like 'my manager suggested' are fatal ownership disqualifiers.
• Use precise individual language instead of collective 'we' to highlight your personal role in decision-making and problem-solving.
• Quantify impact with metrics and business outcomes to show the real-world effect of your decisions.
• Describe how you balanced incomplete data, monitored outcomes, and iterated on your solution to demonstrate sound judgment and learning.
• Avoid vague or generic statements; Amazon Bar Raisers look for concrete examples of weighing risks and benefits and acting decisively with partial data.

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Model Answer Guidance

A strong answer clearly states the candidate’s own initiative to identify and solve a problem without managerial prompting, uses specific individual language, quantifies the impact with metrics, and explains how they monitored and iterated on the solution. For example, 'I noticed an unusual spike in latency not assigned to my team and no ticket existed; I weighed risks with 70% data, isolated a race condition, implemented a fix reducing latency by 30%, and monitored outcomes to ensure robustness.' This signals ownership, good judgment, and measurable impact aligned with Amazon’s Are Right a Lot principle.