Continuous event stream processing is designed for use cases that require immediate analysis and action on data as it is generated. Fraud detection (A) must analyze transaction patterns in real-time to block malicious activity instantly. Context-aware recommendations (B) leverage a user's live clickstream data to provide relevant suggestions on-the-fly, enhancing user experience. Log monitoring and fault detection (D) continuously ingests and analyzes logs and metrics from distributed systems to identify and alert on anomalies or errors as they occur, enabling rapid response. These applications are inherently event-driven and depend on low-latency, continuous data processing to be effective.

C. End-of-day financial settlement processing: This is a quintessential batch processing workload, operating on a finite set of data at a scheduled time, not a continuous, unbounded stream.

E. Historical dashboards: These typically run analytical queries against data stored in a data warehouse or database, representing a batch or ad-hoc query pattern, not real-time stream processing.

1. Confluent Documentation, "Apache Kafka Use Cases": This official resource explicitly lists the correct answers as primary use cases. It states, "Kafka is used for real-time fraud detection," "Real-time recommendations...by capturing and analyzing user behavior in real time," and "Log analysis and monitoring...Kafka can be used to collect and process log data in real time."

Source: Confluent Documentation, "Learn Apache Kafka", Section: "Apache Kafka Use Cases".

2. Narkhede, N., Shapira, G., & Palino, T. (2021). Kafka: The Definitive Guide (2nd ed.). O'Reilly Media. Chapter 1, "Meet Kafka," introduces the core drivers for Kafka's creation and its common applications. It details "Website Activity Tracking" (the basis for recommendations), "Log Aggregation" (for monitoring), and real-time processing of event data, which is fundamental to fraud detection.

Source: Chapter 1, Sections: "Website Activity Tracking" and "Log Aggregation".

3. Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media. Chapter 11, "Stream Processing," contrasts batch processing with stream processing. It describes use cases like "Complex event processing (CEP)," which includes fraud detection and monitoring systems, as prime examples of stream processing where timely responses to event patterns are critical. End-of-day jobs are used as a counterexample of batch processing.

Source: Chapter 11, Section: "Stream Processing", Subsection: "Applications of Stream Processing".