What are the different types of thresholds that can be set for anomaly detection?

Thresholds for anomaly detection are critical for identifying unusual patterns that may indicate issues within a system. The correct type of thresholds involves monitoring specific metrics that can indicate significant shifts in performance or health.

Focusing on traffic drops, traffic spikes, and failure rates provides a robust framework for detecting anomalies. Traffic drops can indicate potential outages or service disruptions, while traffic spikes could signal unexpected user behavior that might lead to performance degradation or system strain. A failure rate threshold is essential in monitoring application health, as an increase in errors or failures can point towards critical issues that require immediate attention.

The other options, while relevant in their contexts, do not encompass the comprehensive set of thresholds used for effective anomaly detection. For instance, response times and user sessions relate more to user experience metrics but do not capture sudden changes in system behavior. System load and resource utilization focus on infrastructure performance but lack the immediacy of detecting unusual traffic patterns that can lead to service disruptions. Transaction volume and user satisfaction are important metrics for understanding application success but do not emphasize the immediate need to identify and react to abnormal activity in real-time.

Thus, the choice highlighting traffic drops, spikes, and failure rates aligns with the essential criteria for effective anomaly detection in the context of system monitoring.