BenchMark Quizzes by MDFT Pro

Monitor Temperature Data Streams For Anomaly Detection

MDFT Pro, a well-known training agency, operates multiple learning centers with sophisticated climate control systems to ensure optimal learning environments. Claire, the Facilities Technology Manager, needs to implement a monitoring system that analyzes temperature data from multiple sensors throughout their training facilities, including classroom temperatures, server room conditions, and HVAC performance metrics.

The system must detect unusual temperature patterns that could indicate equipment failures, energy inefficiencies, or environmental issues before they impact student comfort and learning outcomes, while requiring minimal custom development effort.

What should Claire include in the solution to monitor temperature data streams and generate alerts for atypical values?

Choose the correct answer from the options below.

Explanations for each answer:

Multivariate Anomaly Detection is correct. Multivariate Anomaly Detection analyzes multiple related data points simultaneously, making it ideal for temperature monitoring systems where multiple sensors and environmental factors interact to determine normal vs. abnormal conditions.
Metric alerts in Azure Monitor is incorrect. Azure Monitor metric alerts work with predefined thresholds and don't provide the advanced machine learning capabilities needed to detect subtle anomalies in temperature patterns.
Azure Stream Analytics is incorrect. Stream Analytics processes real-time data but requires custom anomaly detection logic, increasing development effort compared to pre-built AI services.
Univariate Anomaly Detection is incorrect. Univariate Anomaly Detection analyzes single data streams independently, which is less effective for temperature monitoring where multiple environmental factors influence normal readings.

Learn more about Multivariate Anomaly Detection:

Multivariate Anomaly Detection

Exam Preparation Quiz

Monitor Temperature Data Streams For Anomaly Detection