MLA-C02 practice question: Prompt engineering and fine-tuning
ML Model and Foundation Model (FM) Development 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.
A dental chain fine-tuned a foundation model in Amazon Bedrock on 5,000 examples of patient-support replies. The tuned model now writes accurate replies, but its summaries of general health articles have become noticeably worse than the base model's.
The chain wants to keep the patient-support behavior and regain general summarization quality. It cannot collect more patient-support examples, and it has budget for only one more fine-tuning run.
Which TWO changes should the team make to the next fine-tuning run? (Choose two.)
Choose two answers.
AA. Train for more epochs on the 5,000 patient-support examples only, so the model fits this domain more strongly.
BB. Add a set of general-purpose summarization examples to the training data, mixed with the patient-support examples.
CC. Replace the fine-tuning with a much larger learning rate, so the model adapts quickly and forgets less of its earlier training.
DD. Use a lower learning rate and fewer epochs, so that the model's weights change less from the base model.
EE. Delete the base model from the account, so that the team can focus only on the tuned version of the model.
Show the answer and explanation
Answers: B, D
A. Wrong: More epochs on one narrow dataset deepen the specialization and make the forgetting worse. The stem asks the model to keep its general skills.
B. Right: Correct. Mixing general examples into fine-tuning data keeps the model practicing skills outside the narrow task, which reduces catastrophic forgetting. The team still gets its domain behavior.
C. Wrong: A larger learning rate usually overwrites more of the base model's behavior, so it increases forgetting. The option works against the goal.
D. Right: Correct. Smaller updates and fewer passes over the data keep the weights closer to the base model, which limits catastrophic forgetting. The domain behavior is still learned from the examples. Learn more: Custom model hyperparameters (opens in a new tab)
E. Wrong: Deleting the base model does not change how the tuned model behaves, and it does not restore general skills. The option is unrelated to the forgetting problem.
Explanation
Catastrophic forgetting happens when fine-tuning overwrites general skills. Mixing general examples into the training data and using a lower learning rate with fewer epochs keep the weights closer to the base model, so the tuned model keeps its domain behavior and regains general ability.
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