Tech SkillsValidation and Learning

Offline Eval Set 40 Question Explained

This worked AI Engineer Interview practice question tests Validation and Learning. Try the answer first, then review why A is the best choice.

Short answer

The correct answer is A. Whether the eval set covers the traffic that is now failing — 40 examples is far too small to generalize..

When two signals disagree, audit what each one actually measures — a 40-example eval almost certainly under-covers the failing traffic, and its coverage is checkable today. The key is to match the exact action in the prompt to the tool, command, or concept that performs that action directly. Here, A. Whether the eval set covers the traffic that is now failing — 40 examples is far too small to generalize. is the only option that satisfies the scenario without adding unrelated behavior.

The Question

Your offline eval set of 40 examples says the new model is better. Support tickets say otherwise. What do you check first?

AWhether the eval set covers the traffic that is now failing — 40 examples is far too small to generalize.Correct
BWhether support agents are misattributing unrelated issues to the model swap.
CWhether the provider silently changed the model behind the endpoint.
DWhether ticket volume is up across all categories, not just model-related ones.

Why A is correct

When two signals disagree, audit what each one actually measures — a 40-example eval almost certainly under-covers the failing traffic, and its coverage is checkable today. The key is to match the exact action in the prompt to the tool, command, or concept that performs that action directly. Here, A. Whether the eval set covers the traffic that is now failing — 40 examples is far too small to generalize. is the only option that satisfies the scenario without adding unrelated behavior.

Why the other options are wrong

Option B: Whether support agents are misattributing unrelated issues to the model swap.

Option B does not match the requirement in the prompt. The correct answer is A because When two signals disagree, audit what each one actually measures — a 40-example eval almost certainly under-covers the failing traffic, and its coverage is checkable today.

Option C: Whether the provider silently changed the model behind the endpoint.

Option C does not match the requirement in the prompt. The correct answer is A because When two signals disagree, audit what each one actually measures — a 40-example eval almost certainly under-covers the failing traffic, and its coverage is checkable today.

Option D: Whether ticket volume is up across all categories, not just model-related ones.

Option D does not match the requirement in the prompt. The correct answer is A because When two signals disagree, audit what each one actually measures — a 40-example eval almost certainly under-covers the failing traffic, and its coverage is checkable today.

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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