Tech SkillsValidation and Learning

LLM Router Right 96 Question Explained

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

Short answer

The correct answer is B. Treat security-sensitive intents as a non-compensating slice with its own severity-weighted miss rate, adversarial test set, and uncertainty-based routing to specialists..

Aggregate accuracy is dominated by easy intents, so it can rise while the rare dangerous miss gets worse — the risky slice needs its own gate and its own adversarial data. The key is to match the exact action in the prompt to the tool, command, or concept that performs that action directly. Here, B. Treat security-sensitive intents as a non-compensating slice with its own severity-weighted miss rate, adversarial test set, and uncertainty-based routing to specialists. is the only option that satisfies the scenario without adding unrelated behavior.

The Question

An LLM router is right 96% of the time but occasionally sends account-takeover reports to the general queue, and confirmed outcomes arrive weeks later. How do you gate releases?

AHold releases until the 96% aggregate improves to 99%.
BTreat security-sensitive intents as a non-compensating slice with its own severity-weighted miss rate, adversarial test set, and uncertainty-based routing to specialists.Correct
CAdd a second model to double-check every routing decision.
DWait for the delayed outcome labels and gate on confirmed miss rate.

Why B is correct

Aggregate accuracy is dominated by easy intents, so it can rise while the rare dangerous miss gets worse — the risky slice needs its own gate and its own adversarial data. The key is to match the exact action in the prompt to the tool, command, or concept that performs that action directly. Here, B. Treat security-sensitive intents as a non-compensating slice with its own severity-weighted miss rate, adversarial test set, and uncertainty-based routing to specialists. is the only option that satisfies the scenario without adding unrelated behavior.

Why the other options are wrong

Option A: Hold releases until the 96% aggregate improves to 99%.

Option A does not match the requirement in the prompt. The correct answer is B because Aggregate accuracy is dominated by easy intents, so it can rise while the rare dangerous miss gets worse — the risky slice needs its own gate and its own adversarial data.

Option C: Add a second model to double-check every routing decision.

Option C does not match the requirement in the prompt. The correct answer is B because Aggregate accuracy is dominated by easy intents, so it can rise while the rare dangerous miss gets worse — the risky slice needs its own gate and its own adversarial data.

Option D: Wait for the delayed outcome labels and gate on confirmed miss rate.

Option D does not match the requirement in the prompt. The correct answer is B because Aggregate accuracy is dominated by easy intents, so it can rise while the rare dangerous miss gets worse — the risky slice needs its own gate and its own adversarial data.

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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