AWS Certified AI Practitioner AIF-C01 Practice Question
A company is developing a voice assistant that must run entirely on smart speakers with limited memory and CPU resources. When evaluating pre-trained foundation models, which selection criterion will most strongly determine whether the model can run locally on the device?
Running a model on edge hardware with constrained compute and memory depends primarily on the model's size and the resources it requires for inference. Smaller, less complex models need fewer parameters, lower RAM, and less processing power, making them suitable for local execution. Criteria such as multi-lingual support, maximum input/output length, or the temperature parameter affect functionality or response style but do not directly indicate whether the model will fit and perform on resource-limited devices.
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What does ‘model size’ mean in the context of AI models?
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What does ‘resource requirements’ include for running AI models locally?
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Why is multi-lingual support not a determining factor for model suitability on edge devices?
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Why is model size critical for running AI on resource-limited devices?
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What techniques are used to reduce the size of machine learning models?
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How do resource limits of edge devices affect AI model performance?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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