The requirement:
You have a system monitoring temperature data from a single data stream.
The system must generate an alert in response to atypical values.
The solution must minimize development effort.
Analysis of Options:
Univariate Anomaly Detection
Designed for detecting anomalies in a single time series (e.g., temperature, pressure, humidity, sales).
Since the system monitors temperature only, this is a classic univariate case.
Provides simple integration with minimal effort.
Correct choice.
Azure Stream Analytics
Can process real-time streaming data and detect conditions (with custom queries).
However, it requires more development effort since you would need to manually code logic for anomaly detection.
Not the minimal-effort option.
Metric alerts in Azure Monitor
Used to trigger alerts on predefined thresholds (static rules).
Not intelligent anomaly detection-does not automatically learn patterns from data.
Would require manual threshold setup, which is not efficient for anomaly detection.
Multivariate Anomaly Detection
Used when analyzing multiple related time series (e.g., temperature, pressure, vibration all together).
Since this scenario only has one variable (temperature), multivariate is unnecessary and adds complexity.
The answer: A. Univariate Anomaly Detection
Microsoft References
Azure AI Anomaly Detector - Univariate
Multivariate Anomaly Detector
Difference between univariate and multivariate anomaly detection