AWS Certified AI Practitioner AIF-C01 Practice Question
A startup runs a generative-AI customer-support chatbot on Amazon Bedrock. During peak hours they want the lowest possible latency and are comfortable paying more to achieve it. Which invocation option should they choose?
Use Bedrock provisioned throughput with dedicated capacity.
Deploy the model with SageMaker asynchronous inference endpoints.
Process requests through Amazon SageMaker batch transform.
Provisioned throughput on Amazon Bedrock allocates dedicated model capacity to one customer, delivering consistently low and predictable latency. The trade-off is a higher, fixed hourly cost. On-demand invocations share capacity with other tenants, so latency can increase when traffic is heavy even though costs stay usage-based. SageMaker batch transform and asynchronous inference are designed for non-interactive or queued workloads and add processing delay, making them unsuitable when minimal end-user latency is the priority.
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What is Amazon Bedrock provisioned throughput?
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Why does latency matter for generative-AI chatbots?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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