AWS Certified AI Practitioner AIF-C01 Practice Question
A company wants to limit the carbon footprint of a new generative-AI project on AWS. Which model-selection practice best supports this environmental goal?
Run the same model-training job in several AWS Regions to provide redundancy during development.
Select the smallest pre-trained model that meets the solution's accuracy needs instead of training a larger model from scratch.
Choose a model that requires dedicated on-premises hardware running continuously, even when not serving requests.
Train an extra-large model from scratch on multiple GPU clusters to maximize top-end accuracy.
Choosing a smaller pre-trained model that already satisfies the application's accuracy requirements avoids the energy-intensive process of training a very large model from scratch. This reduces total compute time, electricity consumption, and associated carbon emissions. Retraining large models, running hardware 24/7 when not needed, or duplicating training jobs across Regions all increase resource use and environmental impact without adding sustainability benefits.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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