AWS Certified AI Practitioner AIF-C01 Practice Question
A hospital needs an ML solution that analyzes patient vital signs continuously and triggers alerts within one second when anomalies occur. Which inferencing approach is most appropriate?
Real-time (online) inference with a low-latency endpoint
Batch inference that runs once each hour on accumulated data
Offline model training jobs executed nightly
Batch inference performed daily on archived patient records
Real-time (online) inference is designed for use cases that require very low-latency predictions as data arrives, such as monitoring patient vitals and issuing immediate alerts. Batch inference processes records in groups on a set schedule, so hourly or daily jobs would introduce unacceptable delays. Offline training produces or updates a model but does not deliver predictions to end users in real time, so it also fails to meet the one-second requirement.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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