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MLA-C02 practice question: Preparing data for fine-tuning and distillation

Data Preparation for ML and AI A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A library system wants a small, inexpensive model to answer patron questions about borrowing rules, in the same style as a large foundation model the team already uses for these questions. The team has about 40,000 past questions but no human-written answers.

The new model must run at low cost in production, and the data must stay in the team's own Amazon S3 bucket.

Which approach meets these requirements?

Choose one answer.

  1. A. Hire annotators to write answers for all 40,000 questions, then fine-tune the small model on those human answers.
  2. B. Run the large model on every patron question in production and cache its answers, so that a smaller model is never needed.
  3. C. Train a small model from scratch on the 40,000 questions alone, with no answers, using an unsupervised clustering algorithm.
  4. D. Use Bedrock model distillation with the large model as teacher and prompts stored in S3, so the teacher generates training responses for the small model.
Show the answer and explanation

Answer: D

  • A. Wrong: Writing 40,000 answers by hand is slow and costly, and the stem gives the team a large model to learn from. The team does not need human answers to get useful training targets.
  • B. Wrong: Caching large-model answers does not produce a smaller model, and the large model still runs at its higher cost. The option does not meet the cost goal.
  • C. Wrong: Clustering groups questions but does not produce answers in the large model's style. The stem asks for the behavior of the large model to be transferred.
  • D. Right: Correct. Distillation uses prompts as its core input and has the teacher model generate responses, so the team needs no human labels. The job reads the prompts from the team's bucket. Learn more: Customize a model with distillation in Amazon Bedrock (opens in a new tab)

Explanation

Bedrock model distillation takes prompts, has a large teacher model generate responses, and fine-tunes a smaller student model on them, so no human labels are needed. Caching answers or clustering questions does not produce a cheaper model that answers in the teacher's style.

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