AWS Certified AI Practitioner AIF-C01 Practice Question
A startup wants to fine-tune a foundation model on Amazon Bedrock using one year of customer-support chat logs. To make sure the training data is representative, which initial action should the team take?
Compress the raw log files with Gzip before uploading them to Amazon S3.
Convert all chat text to lowercase to reduce the vocabulary size before training.
Strip stop words and punctuation from the logs to minimize token count.
Select a balanced subset that contains interactions from each customer segment, support channel, and issue type.
Before fine-tuning, the dataset should reflect the full variety of situations the model will encounter. Sampling so that messages from every customer segment, support channel (chat, email, phone transcripts), and issue category are included provides balanced coverage. Lowercasing text, removing stop words, or compressing files are useful preprocessing or storage steps, but they do not address whether the examples represent the real-world distribution of conversations the model must handle.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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