AIF-C01 Practice Question 39: Sustainable Model Choice

AWS Certified AI Practitioner (AIF-C01) · Test 4 · question 9 of 10

Guidelines for Responsible AIeasy

A company runs a narrow, well-defined text-classification task on the largest foundation model it could find. Tests show that a much smaller model tuned for the task reaches comparable accuracy. The company has a goal of reducing the environmental impact of its AI workloads. Which approach best supports that goal while still meeting the task's requirements?

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Answer:

C. Switch to the smaller task-tuned model, using techniques such as model distillation

The AWS Well-Architected Generative AI Lens recommends leveraging smaller models and optimized inference techniques to reduce carbon footprint. Instead of relying on large, general-purpose models, fine-tuning smaller models tailored to specific use cases can often yield comparable results with fewer computational resources. Model distillation trains a smaller model to mimic a larger one, keeping similar performance while needing fewer resources. For a narrow task where the small model already performs well, it is the responsible choice (option C).

- A is wrong because the stem shows the premise is false for this task. A larger model uses more compute for every request without a meaningful accuracy gain. - B is wrong because adding instances to a large model *increases* total resource and energy use. It trades latency for a bigger footprint. - D is wrong because pre-training a new, larger model is the most compute-intensive option there is, the opposite of the sustainability goal.

Reference: GENSUS03-BP01 Leverage smaller models and optimized inference techniques to reduce carbon footprint

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