AWS Certified AI Practitioner AIF-C01 Practice Question
An e-commerce site wants to embed a real-time product-recommendation chat powered by an Amazon Bedrock foundation model. During model selection, which criterion is the most important to ensure a smooth customer experience?
Built-in capability to generate and edit short video clips
Low inference latency to maintain quick back-and-forth responses
Access to a large collection of pre-labeled training images
Support for fine-tuning using 16-bit floating-point weights
Because the chat must feel interactive, the model has to return responses quickly. Low inference latency is therefore the primary consideration. Large labeled image sets and video-generation features do not improve a text-based chat. Although fine-tuning options can be useful, the precision of weight formats is less critical than response time for this real-time use case.
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What is inference latency?
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Why is low inference latency more important than features like fine-tuning or video generation in real-time chat applications?
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How does Amazon Bedrock help reduce inference latency in applications like chatbots?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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