AWS Certified AI Practitioner AIF-C01 Practice Question
A startup is adding a text-completion feature to its mobile app. To prevent noticeable typing lag, inference responses must return in well under 200 ms. When reviewing foundation models available in Amazon Bedrock, which characteristic should the team prioritize to achieve the lowest end-to-end latency?
Select a model with a relatively small parameter count optimized for lightweight tasks.
Pick a model that offers the longest input context window.
Use a model that can generate both text and images.
Choose a model that is trained for multilingual output.
Inference latency is largely driven by how much computation the model must perform. Smaller models with fewer parameters require less GPU memory and fewer operations per token, so they typically generate tokens faster and return responses sooner. Multilingual support, extended context windows, or extra modalities add capabilities but do not inherently shorten latency-and can even increase it because the models are larger or more complex.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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