AWS Certified AI Practitioner AIF-C01 Practice Question
To fine-tune a foundation model for a multilingual customer-support chatbot, a data scientist must ensure the training data is representative of the messages the chatbot will handle in production. Which data-preparation strategy most directly supports this goal?
Remove all entries that contain spelling errors before training.
Exclude rare issue categories to simplify and balance the dataset.
Duplicate high-value English transcripts to increase their weight in the dataset.
Sample chat transcripts in proportion to each supported language and major issue category.
A representative dataset mirrors the distribution of inputs the model will see after deployment. Sampling chat transcripts so that each supported language and major issue category appears in the same proportion as real traffic captures that distribution. Removing all spelling errors may eliminate useful signal about noisy user input. Duplicating only English records or dropping infrequent categories skews the dataset, causing the fine-tuned model to underperform on less-represented languages or questions.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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