AWS Certified AI Practitioner AIF-C01 Practice Question
A data science team is preparing a training dataset for a customer-support chatbot. They need to confirm the text samples are accurately tagged with the correct sentiment classes before model training. Which activity is the most appropriate way to validate labeling quality in this context?
Have experienced reviewers audit a random subset of the dataset and compare each label to the documented sentiment guidelines.
Tokenize every sentence and convert all words to lowercase to standardize the text.
Store the dataset in Parquet format to speed up future processing jobs.
Remove duplicate sentences from the corpus by hashing each text record.
Reviewing a random sample of the records against clear labeling guidelines lets humans verify that each text example carries the correct sentiment tag. This directly measures labeling accuracy and reveals systematic issues that could harm model performance. Removing duplicates, normalizing text, or changing the file format improve cleanliness or efficiency but do not confirm that the labels themselves are correct.
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Why is auditing a random subset better for validating labeling quality?
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What are documented sentiment guidelines?
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How would poor labeling affect model performance?
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What are sentiment classes in the context of machine learning?
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Why is auditing a random subset of data samples important for data quality validation?
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What is the significance of documented sentiment guidelines?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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