Model Based Grader Says Question Explained
This worked AI Engineer Interview practice question tests Validation and Learning. Try the answer first, then review why D is the best choice.
Short answer
The correct answer is D. Inspect the disagreement cases by rubric dimension, calibrate the grader against adjudicated expert labels, and rerun a preregistered comparison..
Disagreement between instruments is data: the cases where they diverge tell you which one is miscalibrated and on what. The key is to match the exact action in the prompt to the tool, command, or concept that performs that action directly. Here, D. Inspect the disagreement cases by rubric dimension, calibrate the grader against adjudicated expert labels, and rerun a preregistered comparison. is the only option that satisfies the scenario without adding unrelated behavior.
The Question
A model-based grader says the new assistant is better; domain experts prefer the old one on a blinded sample. Both sides want their metric to win. What settles it?
Why D is correct
Disagreement between instruments is data: the cases where they diverge tell you which one is miscalibrated and on what. The key is to match the exact action in the prompt to the tool, command, or concept that performs that action directly. Here, D. Inspect the disagreement cases by rubric dimension, calibrate the grader against adjudicated expert labels, and rerun a preregistered comparison. is the only option that satisfies the scenario without adding unrelated behavior.
Why the other options are wrong
Option A does not match the requirement in the prompt. The correct answer is D because Disagreement between instruments is data: the cases where they diverge tell you which one is miscalibrated and on what.
Option B does not match the requirement in the prompt. The correct answer is D because Disagreement between instruments is data: the cases where they diverge tell you which one is miscalibrated and on what.
Option C does not match the requirement in the prompt. The correct answer is D because Disagreement between instruments is data: the cases where they diverge tell you which one is miscalibrated and on what.
Key idea: Validation and Learning
Every question is a production situation an AI engineer interviewer can put to you: a failing endpoint, a cost spike, an agent that loops, an eval that disagrees with users. The best answer is almost always the one that measures or contains the problem before it changes the model or the prompt. If you miss a question, note which habit it tests: decompose before you fix, put the security boundary where the side effect happens, and trust an eval only after you check what it covers. The 12 open-ended questions with answer guides are at aijobprep.app/questions/ai-engineer.html.
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