Real time credit card fraud scoring that requires millisecond response times is correct because the use case demands immediate, per transaction decisions and an online endpoint supports low latency synchronous inference suitable for that requirement.

An online endpoint is designed for real time scoring and it can return predictions within milliseconds when configured with the right compute and autoscaling settings, which makes it the right choice for fraud detection that must act on individual transactions as they occur.

Scheduled overnight scoring of a 25 TB transaction archive is incorrect because that scenario is a large volume, offline job that is best handled by batch scoring, which is optimized for throughput and cost efficiency rather than millisecond latency.

Hosting the model on Azure Kubernetes Service for gradual canary deployments is incorrect as stated because this option describes an infrastructure and deployment strategy rather than a scoring mode, and it does not directly address the need for millisecond real time responses. Azure Kubernetes Service can host real time endpoints but the question contrasts online endpoints with batch scoring.

Producing monthly sales performance analyses for stakeholder review is incorrect because monthly analytical reports are not latency sensitive and they are better served by batch processing or analytics pipelines that process aggregated data on a schedule.

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