These are Fine-Tuning questions of the kind Amazon actually asks — the patterns reported from Amazon'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.
Amazon Fine-Tuning concept questions
Explain what limits how many concurrent users a single GPU can serve for LLM inference.
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Amazon Fine-Tuning applied & hands-on questions
Your provider releases a new model version claiming better quality at lower cost. Design the process for deciding whether to move, and for moving.
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Your team wants to fine-tune an open model to replace a frontier API for a document-classification feature: 3M documents/month, 6 categories, currently 94% accurate. Argue both sides and recommend.
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How to use this page: Amazon 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 Amazon Fine-Tuning rounds test.