AWS Certified AI Practitioner AIF-C01 Practice Question
A company needs to run a foundation model locally on mobile devices that have only 2 GB of RAM and no dedicated GPU. Which consideration best supports selecting a smaller, less complex model architecture for this use case?
Leveraging emergent advanced reasoning that appears in multi-billion-parameter models
Supporting very long input contexts of tens of thousands of tokens
Achieving the widest possible multilingual accuracy across 200+ languages
Reducing memory and compute requirements to meet on-device latency and footprint limits
Smaller, less complex models require fewer parameters and computational resources, allowing them to load into limited memory and generate outputs quickly on resource-constrained hardware. Larger or more complex models are designed to deliver extended context windows, broader multilingual coverage, or stronger emergent reasoning abilities, but they typically demand far more memory, power, and latency headroom than an edge device can provide.
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What are foundation models?
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Why do smaller models work better on devices with limited resources?
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What is emergent advanced reasoning in AI models?
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Why do smaller models perform better on devices with limited hardware like mobile phones?
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What are the trade-offs of using smaller, less complex models?
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How do smaller models handle inference latency compared to larger models?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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