AWS Certified AI Practitioner AIF-C01 Practice Question
When comparing pre-trained text generation models on Amazon Bedrock, which single attribute most directly indicates that one model's architecture is more complex and will require more compute resources and longer inference times?
The total number of trainable parameters in the model
The licensing terms under which the model is released
The temperature value specified in generation prompts
The length of a typical input prompt used for testing
Model complexity is commonly measured by the total number of trainable parameters. A higher parameter count usually reflects a deeper or wider architecture, which in turn increases memory consumption, compute demand, and latency during inference. Temperature settings, prompt length, and licensing terms do not describe the model's internal architecture; they influence usage, not intrinsic complexity. Therefore, examining the parameter count is the clearest way to judge how demanding a model will be to run.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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