AWS Certified AI Practitioner AIF-C01 Practice Question
A startup plans to deploy a text-classification solution on AWS. The team wants to keep the application's carbon footprint as low as possible while meeting accuracy requirements. Which practice best supports this goal when selecting a model?
Choose the smallest pre-trained model that still meets the application's accuracy targets.
Fine-tune a very large foundation model for many additional epochs to maximize accuracy.
Deploy the model on a dedicated GPU instance and keep it running even when traffic is low.
Train a new deep-learning model from scratch on on-demand GPU instances.
Running inference with a smaller, more efficient model typically requires fewer CPU or GPU cycles, less memory, and shorter processing times than larger models that achieve only marginally higher accuracy. Using less compute directly reduces electricity consumption and therefore lowers associated carbon emissions. By contrast, retraining from scratch, fine-tuning extremely large models, or leaving dedicated GPU instances idle for long periods all increase energy use without necessarily improving environmental sustainability.
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Why does choosing a smaller pre-trained model help reduce carbon emissions?
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What is the difference between training a model from scratch and using a pre-trained model?
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How do idle dedicated GPU instances affect sustainability?
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Why do smaller models consume less energy?
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What is a pre-trained model, and why is it used?
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What are the carbon implications of idle dedicated GPU instances?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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