These are Fine-Tuning questions of the kind Google actually asks — the patterns reported from Google's AI-engineering rounds, where design trade-offs, scale and failure modes matter as much as definitions. Treat this page as a mock interview: say every answer out loud before revealing it. If one surprises you, the lesson behind it is linked at the bottom.
Google Fine-Tuning concept questions
An LLM feature works well for months, then quality drops. No code or prompt changes were deployed. What happened, and how would you have known?
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Google Fine-Tuning applied & hands-on questions
You need to score 2,000 generated outputs a day for quality. Design the judge and the process that makes its numbers trustworthy.
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An interviewer asks you to argue against fine-tuning for a case that sounds like a good fit: a customer wants to fine-tune on 5,000 support conversations to make their bot 'sound like our team'. Make the argument.
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How to use this page: Google rarely asks something you've never seen — they ask a standard Fine-Tuning concept and then push one level deeper ("why?", "what would you do if..."). Master the concept in the Fine-Tuning course lessons, and the follow-up stops being scary.
Keep practising: When to Fine-Tune, LoRA & PEFT, Evaluating LLMs and LLMOps & Monitoring cover what most Google Fine-Tuning rounds test.