AWS Certified AI Practitioner AIF-C01 Practice Question
An AI team is creating a labeled dataset to fine-tune a foundation model for sentiment analysis. Which action will most improve the quality and consistency of the labels before training begins?
Remove punctuation from the text before sending it to annotators to speed up their work.
Store the labels in the file names instead of a separate metadata column.
Use the foundation model itself to auto-label all data without human review.
Provide detailed labeling guidelines and measure inter-annotator agreement, re-labeling records with low consensus.
Clear, shared instructions help human annotators apply the same criteria to every record. Measuring inter-annotator agreement (for example, with Cohen's kappa) reveals items where opinions diverge, allowing the team to review and relabel them. This process yields consistent, trustworthy labels that improve model learning. Auto-labeling without review can propagate errors, storing labels only in file names complicates processing and auditing, and stripping punctuation speeds annotation but does not address label accuracy.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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