AWS Certified AI Practitioner AIF-C01 Practice Question
An ecommerce company wants to automatically create concise captions for product photographs. When comparing available foundation models in Amazon Bedrock, which selection criterion should most influence the choice?
The default maximum sequence length for text prompts
Whether the model supports an image-to-text modality
Availability of a large multi-lingual training corpus
The number of trainable parameters available for fine-tuning
Because the task requires turning an image into descriptive text, the model must accept an image as input and produce text as output. Ensuring the model supports the required input and output modalities is therefore the primary concern. The other criteria-multi-lingual support, fine-tuning parameter count, and maximum text sequence length-may matter later, but none of them matters if the model cannot process the data type in the first place.
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What does 'image-to-text modality' mean in the context of foundation models?
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What is Amazon Bedrock, and how does it relate to foundation models?
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Why are other criteria (like multi-lingual support or fine-tuning parameters) secondary in this scenario?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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