AWS Certified AI Practitioner AIF-C01 Practice Question
A company wants to fine-tune a foundation model to generate concise summaries of its internal technical reports. Before launching the training job, which activity is a recommended part of data preparation to improve the quality of the fine-tuned model?
Remove duplicates and fix formatting issues in the training documents.
Increase the maximum output token length allowed by the model.
Enable Top-P sampling to diversify generation during training.
Freeze half of the model's layers to reduce memory usage during fine-tuning.
Data curation and cleaning-such as removing duplicate or low-quality sentences, correcting formatting errors, and redacting sensitive information-is a key step before fine-tuning. Supplying a clean, representative training set helps the model learn the desired task accurately. Adjusting inference parameters, freezing layers, or changing output length are tuning or deployment actions; they do not prepare the dataset itself.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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