AWS Certified AI Practitioner AIF-C01 Practice Question
To promote inclusivity when training a machine learning model in Amazon SageMaker, which dataset practice should a team prioritize before training begins?
Collect samples that represent the diversity of the intended user population.
Remove statistical outliers from the dataset to reduce variance.
Use only high-resolution images to improve overall prediction accuracy.
Increase the number of training epochs until the validation loss stabilizes.
Inclusivity means the model should work well for the full range of users it will serve. The best way to support this goal is to acquire and label data that reflects the diversity of the target population. Without representative examples from all relevant demographic groups, the model is likely to underperform for under-represented users. The other practices focus on accuracy tuning or data cleaning but do not guarantee that all user groups are present in the dataset, so they do not specifically address inclusivity.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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