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AIF-C01 practice question: Training and fine-tuning (4 of 4)

Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A bank plans to fine-tune a model to draft replies to customer complaints in its house style. Its complaint archive contains customers' names, account numbers and addresses.

Which TWO data preparation practices will MOST improve the result? (Choose two.)

Choose two answers.

  1. A. Use accurate, reviewed example replies that match the complaints the bank handles
  2. B. Add large volumes of unrelated public text so the dataset is bigger
  3. C. Remove or mask customers' personal data from the examples before training
  4. D. Include unreviewed model drafts and label them as ideal replies
  5. E. Keep only complaints about one product to keep the dataset small
Show the answer and explanation

Answers: A, C

  • A. Right: Correct. Fine-tuning learns from the examples it receives, so accurate, reviewed replies teach the house style and the right answers. The examples should reflect the complaints the bank actually handles. Learn more: Customize your model (opens in a new tab)
  • B. Wrong: Unrelated text dilutes the signal the model needs. Relevance and quality matter more than raw volume, so a bigger dataset is not better here.
  • C. Right: Correct. Personal data in training examples can leak into generated replies and creates privacy and compliance risk. Removing or masking it before training keeps customers' data out of the model.
  • D. Wrong: Unchecked drafts may contain errors, and labeling them as ideal teaches the model to repeat those errors. Only reviewed examples should be used as targets.
  • E. Wrong: A narrow sample is not representative of the complaints the bank receives. The model would handle other products poorly.

Explanation

Good fine-tuning data is accurate, relevant to the task, representative of real use, and free of sensitive personal data. Reviewed replies teach the house style, and masking personal data before training reduces privacy and leakage risk.

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