AWS Certified AI Practitioner AIF-C01 Practice Question
During the foundation model lifecycle, a company wants the model to adopt its internal terminology and tone by continuing training on a relatively small, proprietary dataset. Which lifecycle activity accomplishes this goal by updating the model's parameters with the new data?
Fine-tuning continues the training of an existing foundation model on a smaller, domain-specific dataset so that the model's internal weights adjust to the organization's vocabulary, writing style, or task requirements. Embedding creation only produces vector representations of text and does not modify the model's core parameters. Prompt engineering guides the model at inference time without any weight updates. Model evaluation measures performance but does not change how the model is trained.
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What is fine-tuning in machine learning?
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How does fine-tuning differ from embedding creation?
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What is the role of prompt engineering compared to fine-tuning?
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What is fine-tuning in machine learning?
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How does fine-tuning differ from embedding creation?
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Why can't prompt engineering replace fine-tuning?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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