AWS Certified AI Practitioner AIF-C01 Practice Question
A team needs a language model for a customer-support chatbot. They want to minimize the solution's environmental impact without sacrificing required accuracy. Which approach best aligns with sustainability best practices?
Move training and inference to on-premises GPUs to eliminate cloud energy usage.
Choose the smallest pretrained model that still meets accuracy targets, avoiding unnecessary compute.
Keep the model container running at full capacity continuously to reduce cold starts.
Deploy several large models in a load-balanced cluster to handle peak traffic.
Using the smallest pretrained model that still meets accuracy requirements avoids unneeded parameters and compute cycles. Right-sizing models directly lowers energy use and associated carbon emissions, which is a recommended practice in AWS sustainability guidance. Running large models in parallel, keeping containers at full utilization, or shifting all work on-premises generally increases total power consumption and does not improve sustainability.
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Why is using the smallest pretrained model more sustainable?
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What are pretrained models, and why are they important for sustainability?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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