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AIP-C01 practice question: Fine-tuning, LoRA adapters and custom model lifecycle (Bedrock, SageMaker AI, Model Registry)

Foundation Model Integration, Data Management, and Compliance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A furniture retailer writes about 40,000 product listings a year, and its copywriters have approved about 10,000 finished listings as examples of the brand voice and the required JSON layout.

The listings must sound like the brand and follow the same JSON structure every time, so the storefront can parse them. Each listing also contains facts such as dimensions, price and stock status, and these change daily.

The team wants a single approach that keeps the brand voice and layout consistent, keeps the facts current, and keeps each request under the model's context limit.

Which approach meets these requirements?

Choose one answer.

  1. A. Fine-tune a model on the approved listings to learn the voice and JSON layout, and pass each day's product facts in the request from the catalog API.
  2. B. Train a model every night on the full catalog, so that it learns both the brand voice and the day's facts, and skip any lookup step at request time.
  3. C. Use prompt engineering alone, placing all 10,000 approved listings in the system prompt of every request so that the model copies their voice and layout.
  4. D. Fine-tune a model on the product catalog's facts, so that it memorizes each item's price and dimensions, and repeat the fine-tuning every week to keep those facts up to date.
Show the answer and explanation

Answer: A

  • A. Right: Fine-tuning teaches consistent style and structure from examples, which suits the voice and layout need. Daily facts come from the catalog at request time, so they stay current without retraining. Learn more: Submit a model customization job for fine-tuning (opens in a new tab)
  • B. Wrong: A nightly job means facts for products added during the day are unknown until the next run, and the training cost repeats every night. It also mixes changing facts into the model, which is the wrong place for them.
  • C. Wrong: Ten thousand listings are far too many for one context window, and repeating them in every request would be slow and costly. The approach misses the cost and limit requirements.
  • D. Wrong: Fine-tuning does not reliably memorize individual facts, and prices and stock change daily, so weekly training would still leave stale answers. The approach misses the need for current facts.

Explanation

Style and output structure are patterns that fine-tuning learns well from examples, while changing facts belong in the request, pulled from the catalog. Training nightly or weekly keeps facts stale and costly, and putting all examples in every prompt exceeds the context limit.

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