HardAI MLTechnical screen20 min to answer

How would you build an evaluation dataset for an AI support agent?

1Give yourself 20 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify the objective, user, constraints, and acceptable failure modes.
02Structure the response around task distribution, hard cases, rubrics, refresh process.
03Compare credible alternatives and state the decision criteria.
04Finish with validation, monitoring, and what would change the decision.

Reference answer

strong answer

I would first define the outcome and acceptable error boundary. I would explicitly cover task distribution, hard cases, rubrics, refresh process. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

task distribution
hard cases
rubrics
refresh process

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

alternative answer

A second valid approach is to begin with the highest-risk assumption and test it cheaply. I would explicitly cover task distribution, hard cases, rubrics, refresh process. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

task distribution
hard cases
rubrics
refresh process

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

Grade yourself

task distribution

weight 30%

StrongExplains task distribution concretely and ties it to a decision or measurable outcome.

WeakMentions task distribution without implications, trade-offs, or evidence.

hard cases

weight 25%

StrongExplains hard cases concretely and ties it to a decision or measurable outcome.

WeakMentions hard cases without implications, trade-offs, or evidence.

rubrics

weight 25%

StrongExplains rubrics concretely and ties it to a decision or measurable outcome.

WeakMentions rubrics without implications, trade-offs, or evidence.

refresh process

weight 20%

StrongExplains refresh process concretely and ties it to a decision or measurable outcome.

WeakMentions refresh process without implications, trade-offs, or evidence.

Then expect these follow-ups

What would your evaluation dataset contain?
Tests: depth
What if offline and online results disagree?
Tests: adaptability
How would you set the launch threshold?
Tests: adaptability

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