AWS Certified AI Practitioner AIF-C01 Practice Question
A team will fine-tune a foundation language model for a retailer's customer-service chatbot. Which data selection practice best prepares the dataset for responsible fine-tuning?
Collect recent chat transcripts, redact personal identifiers, and remove conversations that violate company policy.
Generate entirely synthetic conversations with the current model and include them without manual review.
Keep all historical chat logs unchanged to maximize the size of the training set.
Use only publicly available Wikipedia articles about retail terminology.
Removing personal identifiers and filtering out policy-violating or off-topic conversations creates a domain-specific, compliant corpus. This improves the model's ability to answer retail queries accurately while reducing privacy risks and the chance that it learns undesirable behavior. Simply retaining all raw logs, relying on unrelated public articles, or using synthetic data without review either adds irrelevant content, preserves sensitive data, or risks compounding model errors, all of which hurt fine-tuning quality.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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